Збирач потоків

This week’s latest iteration of Google’s yearly developer event reiterated the company’s significant AI commitment. What’s different from messaging and examples past? Maturity.

One of the technologies showcased in the most recent edition of my previous-year retrospective series, published on New Year’s Day, was agentic AI. An overview excerpt from that earlier coverage follows:

Here’s what Wikipedia says about AI agents in its topic intro:

“In the context of generative artificial intelligence, AI agents (also referred to as compound AI systems or agentic AI) are a class of intelligent agents distinguished by their ability to operate autonomously in complex environments. Agentic AI tools prioritize decision-making over content creation and do not require human prompts or continuous oversight.”

And what about the aforementioned broader category of intelligent agents, of which AI agents are a subset? Glad you asked:

“In artificial intelligence, an intelligent agent is an entity that perceives its environment, takes actions autonomously to achieve goals, and may improve its performance through machine learning or by acquiring knowledge. AI textbooks define artificial intelligence as the “study and design of intelligent agents,” emphasizing that goal-directed behavior is central to intelligence. A specialized subset of intelligent agents, agentic AI (also known as an AI agent or simply agent), expands this concept by proactively pursuing goals, making decisions, and taking actions over extended periods.”

A recent post on Google’s Cloud Blog included, I thought, I concise summary of the aspiration:

“Agentic workflows represent the next logical step in AI, where models don’t just respond to a single prompt but execute complex, multi-step tasks. An AI agent might be asked to “plan a trip to Paris,” requiring it to perform dozens of interconnected operations: browsing for flights, checking hotel availability, comparing reviews, and mapping locations. Each of these steps is an inference operation, creating a cascade of requests that must be orchestrated across different systems.”

I suggested there that last year’s rapid evolution of agentic AI technology and products based on it wasn’t a one-off; that the maturation and proliferation trends would undoubtedly continue in the coming year and beyond. We’re nearing the 2026 mid-point and, judging from what Google showcased at yesterday’s keynote, I wasn’t offbase with my earlier perpetuation prediction:

But I’m getting ahead of myself…

Android Show: I/O Edition 2026

Google extended the trend it initiated last year by delivering a separate Android-specific showcase one week ahead of the main event:

Company representatives covered a lot of ground in only a bit more than a half hour, including pending enhancements to Android Auto and “Continue On”, an in-beta conceptual clone of Apple’s Handoff. But two other topics particularly caught my eye. Generally speaking, Google is fundamentally integrating Gemini Intelligence even more than previously into the core of both Android and its Chrome browser, including both anticipatory awareness of what you might need next and the agentic “chops” to independently (potentially) tackle such tasks on your behalf.

The central reason why I find this trend interesting is contextual in nature. Both Amazon (again) and OpenAI are reportedly working on smartphones based on brand new AI-based—specifically agentic, generative and personalized—operating systems. Going “clean slate” from a software standpoint does have at least some advantages, conceptually speaking at least, but it also tends to result in a “heavy lift” with respect to application development, internally and especially from a third-party standpoint. Conversely, Google’s building on a longstanding Android foundation.

Consider that contrast, too, in the context of the other key Android Show tidbit that I want to pass along today. Confirming longstanding rumor, Google announced that it is seriously re-engaging in the tablet market with Android (where, to clarify, it remains a “player” today, primarily courtesy of its Samsung partnership, albeit on a limited basis versus Apple iPad alternatives), as well as expanding Android into computing form factors that were traditionally serviced by Chrome OS, all with a new operating system version code-named “Aluminum”.

The coexistence of the two operating systems had always been awkward at best. They’re both built on a Linux foundation, but that’s kind of like saying that a Trabant and a Ferrari both hail from a Ford Model T heritage. I’m not trying here to infer a vehicle-analogous comparison between the two operating systems with respect to “sleekness”, price or anything like that, only generally proffering that they’re notably dissimilar. Different code bases, different development teams and schedules…over time, Android and Chrome OS had increasingly diverged, to their shared detriment.

And what does “Aluminum” mean for Chrome OS fortunes long-term? Unclear; the latter’s only notable success has been in the education market, but it’s been a notable success there, so Google needs to be careful about how it hand-holds these key customers during the transition (which I’d suggest is a matter not of if, but when). Event-delivered reassurances included that support-timeframe schedules for existing Chrome OS-based products would continue to be honored in full, that new Chrome OS-based products were still in the development pipeline from partners, and that at least some existing Chrome OS-based hardware would be upgradeable to whatever the marketing moniker for “Aluminum” ends up being. That said, if new Chrome OS hardware is still being announced when the decade turns in a few years, I’ll be shocked.

Foundation AI evolutions

Now for the main event. AI has been front and center in Google I/O messaging for a while now, as The Verge and I joked about two years back:

@verge

Pretty sure Google is focusing on AI at this year’s I/O. #google #googleio #ai #tech #technews #techtok

♬ original sound – The Verge

And it was more of the same this year. For those of you who’ve been wondering what the term “foundation model” (or variants of that name) means, I’ll start out with a Wikipedia-sourced definition:

In artificial intelligence, a foundation model (FM), also known as large x model (LxM, where “x” is a variable representing any text, image, sound, etc.), is a machine learning or deep learning model trained on vast datasets so that it can be applied across a wide range of use cases. Generative AI applications like large language models (LLM) are common examples of foundation models.

Building foundation models is often highly resource-intensive, with the most advanced models costing hundreds of millions of dollars to cover the expenses of acquiring, curating, and processing massive datasets, as well as the compute power required for training. These costs stem from the need for sophisticated infrastructure, extended training times, and advanced hardware, such as GPUs.

This all in contrast to dataset- and application-specific models. Wikipedia again:

Adapting an existing foundation model for a specific task or using it directly is far less costly, as it leverages pre-trained capabilities and typically requires only fine-tuning on smaller, task-specific datasets.

Last year at I/O, Google shared updates on v2.5 of its Gemini model family (standard, Flash and Pro), which had been introduced a few months earlier. Gemini v3 subsequently arrived last November. And now we’re up to Gemini family v3.5. Commensurate with the update, another term is in circulation for us to sort out: “Frontier model”. NVIDIA with the definition this time:

Frontier models are the most advanced AI models available at a given moment, trained on massive datasets to deliver state-of-the-art performance across many tasks, representing the leading edge of AI capability. They typically power advanced reasoning, image and text generation, and agentic workflows.

Translation: a fancy way of saying “next generation”. Gotta love those marketeers.

More generally, snark aside, I admittedly was particularly gob smacked by this subset of the event-opening keynote remarks by CEO Sundar Pichai:

These stories of how people are using AI are the best measure of progress. To understand the scale at which people are adopting AI, there is another great proxy — tokens, the fundamental units of data our models process, many representing a problem being solved.

Two years ago, we were processing 9.7 trillion tokens a month across our surfaces — a huge number. Last year at I/O, that grew to roughly 480 trillion tokens. Fast forward to today, that number jumped 7x to over 3.2 quadrillion per month. [Editor note: token maxxing? Likely, to a degree. Still…]

It tells an important story about our products and how others are building as well — especially developers and enterprises:

• Over 8.5 million developers are now building new apps and experiences with our models monthly.
• Our model APIs are now processing roughly 19 billion tokens per minute.
• Over the past 12 months, over 375 Google Cloud customers each processed more than one trillion tokens, representing incredible demand for AI from across industries.

Today we have 13 products with over a billion users each. Five of those have more than 3 billion users. [Editor note: and they’re all AI-enhanced, if not AI-centric]

Multimodal and agentic enhancements

Back in December 2024, within a broader attempt to forecast the year to come, I opined:

Large language models (LLMs), which I rightly showcased at the very top of my 2023 retrospective list, are increasingly impressive in their capabilities. But they’re also, admittedly somewhat simplistically speaking, “one-trick ponies”. As their name implies, they’re language-based from both input (typed) and output (displayed) standpoints. If you want to speak to one, you need to first run the audio through a separate speech-to-text model (or standalone algorithm); the same goes for spitting a response back at you through a set of speakers. Analogies to images and video clips, and other sensory and output data, are apt.

Granted, this approach is at least somewhat analogous to human beings’ cerebral cortexes, which are roughly subdivided into areas optimized for language, vision and other processing functions. Still, given that humans are fundamentally multisensory in both input and output schema, any AI model that undershoots this reality will be inherently limited. That’s where newer multimodal models come in. Vision language models (VLMs), for example, augment language with equally innate still and video image perception and generation capabilities. And large multimodal models (LMMs) are even more input- and output-diverse. Think of them as the deep learning analogies to the legacy sensor fusion techniques applied to traditional processing algorithms, which I ironically alluded to in my 2022 retrospective.

Enter the new Gemini Omni multimodal model:

Last year, Nano Banana brought Gemini’s intelligence to image generation and editing. Since then, it’s helped millions of people restore old photos, design from sketches and visualize ideas in ways that weren’t possible before. From the start we built Gemini to be natively multimodal from the ground up, and now we’re taking the next step.

We’re introducing Gemini Omni, where Gemini’s ability to reason meets the ability to create. Omni is our new model that can create anything from any input — starting with video. With Omni, you can combine images, audio, video and text as input and generate high-quality videos grounded in Gemini’s real-world knowledge. You can also easily edit your videos through conversation.

Today, we’re rolling out the first model in the Omni family: Gemini Omni Flash, to the Gemini app, Google Flow and YouTube Shorts. In time we will support output modalities like image and audio.

And what about burgeoning agentic AI assistants such as OpenClaw? How’s that saying go—”Imitation is the sincerest form of flattery”—albeit this time with innate Google services and account-data access?

We’re also introducing Gemini Spark, a 24/7 personal AI agent that helps you navigate your digital life. Spark represents a big shift for Gemini, transforming it from an assistant that can answer your questions into an active partner that does real work on your behalf and under your direction.

Gemini Spark runs on Gemini 3.5 and uses the Antigravity harness. It’s deeply integrated with the Workspace tools you rely on daily, like Gmail, Docs, Slides and more. Even better, because it is a cloud-based agent, Spark keeps working in the background even when you close your laptop or lock your phone. That combination means Spark is ready to take complex tasks off your plate so you can be more present for what matters most.

“Intelligent Eyewear”

Last but not least, a few words about head-located wearables, including those with integrated displays. Google seems to be reluctant to refer to them as “smart glasses” (or VR headsets, for that matter). Gee, I wonder why? And why? Snark off (again). As regular readers may already recall, I’ve been following this market quite closely in recent years, even personally investing in a few trendsetting product examples. And we’ve in-parallel been hearing about (and I’ve been writing about) Google’s Android XR operating system and application suite for augmented, virtual and hybrid reality systems for a while now, too.

Well, the reality behind the hype is finally coming to market starting this fall. Supposedly. Conceptually, they sound a lot like Meta’s counterparts (albeit perhaps a bit sleeker) which I’d suggest have been meaningful from an implementation standpoint since at least the October 2023 unveil of the second-generation AI Glasses. That said, Meta’s success has to date been held back by (among other factors) a dearth of third-party support. Here’s a reality calibration: even if Google and partners’ competitive devices are no better off in this regard, their inherent coordination with the aforementioned “Google services and account data” will still give them a “leg up”. More generally, you’ve got to admit this was one heck of a compelling live demo suite:

We shall see.

Wrapping up

There was plenty more interesting news released at the Tuesday keynote and more broadly across the two-day event (which is still underway as I type these words mid-day on Wednesday). Browse other writeups on the Google event portal page, along with coverage at 9to5Google and elsewhere. And then share your thoughts with me and your fellow readers in the comments!

The post Google I/O 2026: Agentic AI gets serious appeared first on EDN.

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Portable power station design apparently isn’t easy. So suggests this engineer’s most recent two case studies, not to mention the long-term history (kudos to Kurt Vonnegut for the inspiration).

My first writeup last month discussed why one battery (or a few) in a larger cluster always seems to drain faster than the others, and how this imbalance would affect the system powered by that multi-battery cluster. The answer depended in part on whether the batteries were connected in a series, parallel or a hybrid combo of the two, to “boost the effective voltage (serial) and/or increase overall system runtime (parallel)”. Here’s how I concluded that piece:

I’m covering today only situations where the installed batteries are either non-rechargeable or are removed for recharging. Multi-battery packs recharged in situ (while installed inside a portable power unit, for example) translate to an even more complicated scenario involving, among other factors, the critical importance (and difficulty) of balancing the various cells within the likely series/parallel cluster.

At that point in time, for readers who wanted to know more about this topic, I referenced a Vitron Energy white paper I’d previously recommended in that same piece for other reasons, which also explored this topic at length. Here’s an example of what I’m talking about in the form of a quote from that same white paper:

If a large battery bank is needed, we do not recommend that you construct the battery bank out of numerous series/parallel 12V lead acid batteries. The maximum is at around 3 (or 4) paralleled strings. The reason for this is that with a large battery bank like this, it becomes tricky to create a balanced battery bank. In a large series/parallel battery bank, an imbalance is created because of wiring variations and slight differences in battery internal resistance.

And later in that section, as further elaboration:

When creating a lead-acid battery bank with a higher voltage, like 24 or 48V you will need to connect multiple 12V batteries in series. But there is one problem with connecting batteries in series, and this is that batteries are not electrically identical. They have slight differences in internal resistance. So, when a series string of batteries is charged, this difference in resistance will cause a variance in terminal voltages on each battery. Their voltages become “unbalanced”. This “unbalance” will increase over time and will lead to one of the batteries being constantly overcharged while the other battery is constantly undercharged. This will result in a premature failure of one of the batteries in the series string.

Again, I commend the entire white paper to your attention, not only because it delves in greater detail into the topics discussed in the two excerpts I selected but also because in doing so, it covers not only lead-acid but also lithium-based and other emerging chemistries such as “flow”.

I wrapped up that prior writeup by saying that “I’ll likely have more to say about these topics in future posts as well.” I didn’t necessarily think at the time that I’d be revisiting rechargeable in situ batteries this quickly…but then again, I also didn’t think that, a short time later, I’d personally experience what I still suspect was the outcome of an unbalanced multi-cell battery bank (along with a couple of other functional hiccups, details of which I’ll also share).

The DOA DELTA 3 Smart Extra Battery

I’ve had my EcoFlow DELTA 3 “stack”, the combo of a DELTA 3 Plus and its companion Smart Extra Battery, for one day shy of a year as I write these words in late April:

Until recently, all’s been well. It had only received a couple of firmware updates, all of which had been drama-free. And although it won’t seemingly power my refrigerator reliably, Xcel Energy’s increasingly frequent (or at least so it seems) power outages have provided plenty of other opportunities for me to tap into its stored-electron stash. The most recent outage (again as I write this …another is sooner-or-later-likely-sooner inevitable) in mid-March thankfully lasted only a bit more than seven hours, not several days, but alas, the “stack” didn’t survive it.

During the outage, I’d dragged upstairs its DELTA 2 base unit-plus-smart extra battery “stack” siblings to run an interior lights and recharge flashlights and various mobile devices:

When the utility company-sourced premises electricity came back up just after midnight, I took the DELTA 2 “stack” back downstairs to the workbench in the furnace room, its normal on-standby location. I’d left the DELTA 3 gear there; the base unit’s front panel display was now illuminated but that of the smart extra battery wasn’t, nor seemingly was the latter more broadly functional any longer. And looking more closely, I noticed an “Error 726” indicator on the base unit display that I’d never seen before:

I hit up Google search for suggestions, which were scant, dubious (I don’t think “turn off the unit and wait a few hours for the cells to rebalance themselves” makes much if any sense, particularly given that the only way to turn the unit off is to unplug it first) and more generally indeterminate save for the revelation that Error 726 indicates that “cell voltage differences are too great”. My next and increasingly common step was to publish a Reddit post. One respondent pointed me toward some Facebook group traffic that I’d already come across. Another noted that he/she had experienced the same issue after a recent firmware update, which I’d also done. And a third offered a “Possible it’s a bad cell” suggestion. Hold that thought.

Base unit BMS reset attempts were ineffective; it was also running the latest-available firmware:

I hooked up a fan to one of the AC outputs to drain the base unit’s batteries, in the hope that a full recharge might resurrect its cognizance of the smart extra battery. No dice; the base unit worked fine standalone but threw an Error 726 with the smart extra battery connected. So, I reached out to EcoFlow technical support, who confirmed that the smart extra battery had gone bad and offered to send me a replacement in exchange for my failed unit.

Two weeks later, the new smart extra battery was in my hands. Connecting it to the base unit initially resulted in the generation of another error code in the latter, the nebulous “Error 014”:

After a brief panic, and acting on a hunch, I checked to see if the base unit needed a firmware update before the two devices could be sympatico. Indeed, that was the case, two update cycles’ worth, in fact:

And, at least as of today, the setup once again seems to be working OK, leaving me with one lingering question: what went wrong in the first place?

• Was it a hardware failure, such as (but not necessarily) an unrecoverable cell-imbalance issue, in my specific original smart extra battery?
• Was there a broader fundamental hardware flaw in initial smart extra battery units, suggested by the prompt to do a firmware update (which I hadn’t seen before) when I plugged in the replacement?
• Did a firmware update to the base unit initiate the failure sequence in the first place, as the comments of one of the respondents to my Reddit post indicates might be the case?
• Or were the firmware-update timings (both prior to the original-unit failure and after installation of its replacement) purely coincidental?

Reader theories (and broader thoughts) are as-always welcome in the comments! And now, speaking of botched firmware updates…

A DELTA 2 double-whammy

Regular readers may recall that I’ve had issues after doing firmware updates on EcoFlow gear before. Generally speaking, I’ve therefore subsequently waited for an appropriate period, combing Reddit and relevant Facebook groups for posted evidence of others’ troubles, before taking the plunge myself. However, when I got prompted for an update to the DELTA 2 in late February, I (over)confidently decided to plunge ahead absent any preparatory research:

After all, the previous update I’d done to the DELTA 2 back in late December had gone well. I was so overoptimistic, in fact, that I updated my RIVER 2 at the same time:

The RIVER 2 survived the update just fine. The DELTA 2 on the other hand…

I admittedly didn’t immediately notice the issue, because it only happened occasionally. With the combo “awake”, everything seemed to be fine (well, mostly…keep reading). But at some random point after the units displays turned off (I stuck with the default “5 minute” setting), the power LED on the smart extra battery would extinguish, the base unit’s fan would kick on and perpetually run at low speed, and it would unceasingly (generally) or cyclically (briefly) draw ~20W of power from the AC outlet connected to it:

Punch either unit’s front panel power switch and the displays would wake up, the fan would stop and the trickle charge would cease…until after the displays turned back off again, that is. Lather, rinse, repeat. The trickle-charge behavior particularly worried me, because I didn’t want to end up with an overcharged, overheated and potentially exploded-and-burning battery situation on my hands. So, after several cycles of BMS resets and draining-then-recharging the battery sets, all of which “fixed” the issue only temporarily, I reached out to EcoFlow tech support once again with the proactive suggestion that a firmware downgrade might be in order.

They agreed. I never received (again, keep reading) their first attempt to “push” me a rollback from v1.0.2.176 to firmware v1.0.2.163, but the second attempt was successful:

The DELTA 2 “stack” is once again stable, at least from a charging standpoint. And although it took a while for another invitation to update back to firmware v1.0.2.176 to appear, leaving me wondering if either my “rollback” package had been customized to suppress the subsequent update or EcoFlow had pulled firmware v1.0.2.176 completely:

Turns out I just didn’t wait long enough (and yes, I declined when the update invitation eventually arrived):

Unfortunately, EcoFlow doesn’t publish firmware version histories for its devices, so I couldn’t check for an answer to my question that way. And customer service’s silence in (non-)response to my repeated queries about this particular topic weren’t helpful either (in contrast, I’m compelled to note, to their overall general excellent support).

Connectivity troubles

While I was in communication with EcoFlow, I also brought up an unrelated DELTA 2 issue that I’d been having on-and-off, albeit seemingly more frequently with the passage of time, with the base unit. After some random time period, hours-to-days after I’d established Wi-Fi connectivity, it’d drop Wi-Fi and revert to Bluetooth-only communication with my controlling smartphone:

Other times, even when Wi-Fi was supposedly still operational:

the DELTA 2 base unit, therefore entire “stack”, became “invisible” when I was attempting to reach it from outside my LAN via the EcoFlow “cloud” intermediary:

EcoFlow tech support suggested that the IoT module inside the unit might be gradually failing. I almost didn’t bother pressing the issue further—returning the unit for repair or replacement is something of a hassle, further complicated by the fact that the included flammable batteries mean that I can’t just drop it off at a FedEx Office location but need to arrange for front-door pickup, and then there’s the delay for a replacement unit to arrive—while the loss of Wi-Fi connectivity is annoying, it’s not a functional “death sentence”.

But then EcoFlow confirmed what I’d already suspected, that the IoT module also implements the Bluetooth subsystem, whose functional loss would completely sever further communication with the unit. Couple that with the fact that I’ve been promised a brand-new (not refurbished) replacement, with a zero-cycle fresh battery pack, and it was an offer I couldn’t refuse. I’m awaiting a return-shipping label as I type these words; I’ll report back on the status of the replacement unit via a posted comment on this post once published.

Design is hard

You may have already noticed a commonality to both primary issues noted in this writeup, as well as those in prior EcoFlow problem-themed coverage from me: the smart extra battery. I’m guessing that it’s relatively uncommon for base unit owners to also have this additional-charge (not to mention additional credit card charge) storage peripheral. As such, the prevalence of user problems is also likely to be uncommon. Therefore, I suspect, it’s relatively easy for smart extra battery firmware-related issues in particular to slip through any EcoFlow pre-release testing cracks.

In no way am I making excuses for the various EcoFlow issues I’ve come across; I’m just striving to be pragmatic about root causes. As I noted at the beginning of this writeup, battery pack design is fundamentally challenging. Make a device increasingly “smart” and the level of difficulty further ramps up. Is EcoFlow unique in this regard? I don’t know. I welcome feedback from owners of other manufacturers’ portable power stations (as well as from both EcoFlow and other manufacturers’ representatives themselves) regarding their comparative reliability. And I’d also appreciate insights from other EcoFlow owners re the commonality-or-not of their experiences. Sound off with your thoughts in the comments, please!

—Brian Dipert is the associate editor, as well as a contributing editor, at EDN.

Related Content

• Single-battery failures in multi-battery arrangements: diagnosing selective cell derangements
• EcoFlow’s DELTA 3 Plus and Smart Extra Battery: Product line impermanence curiosity
• EcoFlow’s Delta 2: Abundant Stored Energy (and Charging Options) for You
• Portable power station battery capacity extension: Curious coordination
• Firmware-upgrade functional defection and resurrection

The post More EcoFlow woes: So it goes appeared first on EDN.

"Міська мозаїка" Лариси Пуханової в музеї КПІ kpi чт, 05/21/2026 - 14:40

Manufacturing is at a pivotal moment. Global supply-chain volatility, increasing energy costs, workforce shortages, and growing expectations for quality and customization are forcing factories to rethink how they operate. Traditional automation, optimized for predictability and repetition, struggles to cope with today’s variability and speed of change.

The smart factory represents a decisive shift: production environments that can sense, interpret, and adapt in real time. Central to this shift are three tightly connected technology domains: machine vision, intelligent sensing, and edge AI. Together, they enable factories not just to collect data, but to turn it into insight and action where it matters most.

Figure 1 The notion of smart factory marks a decisive shift in modern manufacturing. Source: Renesas

The limits of conventional automation

Conventional automation systems excel at executing predefined logic. However, they are inherently reactive. When processes drift, materials vary, or equipment degrades, intervention is often manual, time‑consuming, and costly.

Key pressures accelerating the move toward smarter automation include:

• Greater product diversity driven by mass customization
• Higher quality expectations that allow little tolerance for defects
• Skilled labor shortages across engineering and maintenance roles
• Soaring downtime costs, particularly in highly automated lines

Addressing these challenges requires automation systems that are more perceptive and context-aware systems capable of learning from data rather than simply enforcing rules.

Below is a quick recap of smart factory’s three key design building blocks: machine vision, intelligent sensing, and edge AI.

Machine vision: From inspection to interpretation

Machine vision is one of the most visible pillars of the smart factory. Once limited to basic presence checks or rigid defect criteria, today’s vision systems can interpret complex scenes and adapt to variation.

Seeing beyond pass or fail

Traditional, rule-based vision systems perform well under tightly controlled conditions but tend to break down when lighting, materials, or product designs change. Modern vision approaches increasingly incorporate learning-based techniques that recognize patterns instead of relying on fixed thresholds.

Figure 2 Modern vision systems recognize patterns instead of relying on fixed thresholds. Source: Renesas

This evolution enables machines to distinguish acceptable variation from true defects, adapt to new product versions with minimal retraining, and provide richer information for downstream decision-making.

Broader roles on the factory floor

Machine vision now plays a central role in:

• In-line quality assurance, detecting cosmetic, structural, and assembly issues
• Robot guidance, enabling flexible pick-and-place and assembly operations
• Traceability, supporting serialization and regulatory compliance
• Safety monitoring, detecting unsafe conditions or human proximity

As processing moves closer to where images are captured, vision becomes more responsive and resilient, key traits for real-time factory environments.

Figure 3 Machine vision technology is quickly acquiring the key traits required in real-time factory environments. Source: Renesas

Intelligent sensing: Adding awareness to automation

While machine vision provides visual insight, intelligent sensing fills in the rest of the picture. Parameters such as vibration, temperature, current, torque, pressure, and acoustics reveal what is happening inside machines and processes.

From measurement to meaning

Intelligent sensors are no longer passive components. Increasingly, they embed local processing and diagnostics, enabling them to filter and contextualize raw signals, detect subtle behavioral changes, and reduce unnecessary data transmission.

Instead of reporting isolated values, sensors can now indicate conditions such as early wear, imbalance, or inefficiency.

The power of sensor fusion

True process understanding emerges when multiple sensor types are combined. By correlating visual data with physical and environmental measurements, factories gain a far more reliable and nuanced view of operations.

For example, a visual anomaly combined with abnormal vibration data may indicate tool degradation rather than a material flaw. This holistic view reduces false alarms and accelerates corrective action.

Edge AI: Intelligence at the point of action

Edge AI ties machine vision and intelligent sensing together, enabling factories to interpret complex data locally, without relying on constant cloud connectivity.

Why the edge matters

Manufacturing environments demand capabilities that centralized systems struggle to provide:

• Low-latency decision-making for time-critical control
• Operational autonomy in environments with limited connectivity
• Data sovereignty and IP protection
• Scalable deployment across many machines and lines

Edge AI meets these needs by bringing inference and decision logic directly to machines.

Figure 4 Edge AI, the third key building block in smart factory designs, ties machine vision and intelligent sensing. Source: Renesas

Practical impact on operations

With edge AI, factories become more intelligent and proactive in their operations. Instead of reacting to problems after they occur, systems can predict potential failures in advance and help avoid costly disruptions. Processes can also be adjusted in real time to account for changes in materials or environmental conditions, ensuring consistent quality and efficiency.

In addition, AI-driven systems can identify unusual patterns and anomalies that were not explicitly programmed, enabling earlier detection of issues. At the same time, more intuitive and responsive human–machine interactions improve safety and usability on the shop floor. Altogether, this represents a clear shift from reactive control toward adaptive, self-optimizing operations.

Convergence: Creating intelligence through integration

The greatest gains emerge when machine vision, intelligent sensing, and edge AI are designed as a unified system rather than isolated capabilities.

Consider a high-mix production line:

• Machine vision identifies subtle quality deviations
• Intelligent sensors monitor mechanical and electrical behavior
• Edge AI correlates these inputs to identify emerging issues

Instead of scrapping products or stopping the line, the system can adjust in real time, maintaining quality while maximizing throughput. This distributed intelligence also simplifies factory architectures. Decisions are made close to the process, improving responsiveness and system robustness.

Designing for sustainable smart factories

Achieving this level of intelligence is not just a technical challenge, it is a system and ecosystem challenge. Manufacturers need platforms that simplify integration across sensing, processing, connectivity, and security, while supporting long product lifecycles typical of industrial environments.

As adoption accelerates, successful smart factory strategies share several traits:

• Scalability, allowing intelligence to be added incrementally
• Interoperability, avoiding vendor lock-in
• Lifecycle support, including long-term availability and maintenance
• Energy-efficient design, balancing performance with sustainability

Smart factories built on these principles are better equipped to adapt, not just to current challenges, but to future uncertainty.

In the final analysis, smart factory is not defined by a single technology, but by how technologies work together. Machine vision gives machines eyes. Intelligent sensing provides awareness. Edge AI delivers understanding.

With the right enablement and ecosystem support, manufacturers can move beyond reactive automation toward systems that continuously learn, adapt, and improve. In doing so, they transform data into decisions, and factories into resilient, future-ready operations.

Suad Jusuf is director of product marketing at Renesas Electronics. His work centers on defining distinctive value, empowering differentiation, and accelerating customer success through integrated MCU/MPU platforms, AI tools, and system‑level enablement and offerings.

Special Section: Smart Factory

• Rethinking machine vision in industrial automation
• Smart factory: The rise of PoE in industrial environments
• Precision lasers boost safety and efficiency in smart factories
• Tale of 3 sensors operating in smart factory environments
• From edge AI to physical AI in smart factories: A shift in how machines perceive and act
• Robots: Why AI alone will not deliver the next leap in automation
• How emerging robotics standards will shape next-gen automation

The post How machine vision, intelligent sensing, and edge AI are powering smart factory appeared first on EDN.

Круглий стіл "Хроніки Чорнобиля" у ДПМ Інформація КП ср, 05/20/2026 - 23:51

Конференція «Використання ШІ в публічному управлінні: виклики, можливості, перспективи» kpi ср, 05/20/2026 - 23:14

As a follow-up to the toy oscilloscope I designed here, I designed and built something that more closely resembles a real oscilloscope! I included some shots of the build process, all done at home by hand with a hot air station and a preheater. It has 2 channels, each running an ADC3908 off of a shared clock at anywhere from 1MS/s to 62.5MS/s. I wanted to use the 125MS/s version of the part but since I'm still using the Pi for all of the data acquisition and processing, this is about as fast as you can possibly go. The front-end was supposed to have ~30MHz of analog bandwidth but since I had to remove the filter caps after assembly, I think theoretically it has whatever the bandwidth is at the ADC inputs. All of the analog components before the ADC have higher bandwidth. It supports input full-scale ranges from +/-33mV to +/-180V, though I'm hesitant to plug something I made into mains power. It should be isolated as all power comes from the Pi, either through a wall plug or USB powerbank, but I'm still wary. I'll probably try it one day though. It wound up costing way more than I would have hoped, and I probably chose some components that were more expensive than necessary. For example: the two linear regulators I used for the analog supply rails are pricy because of their very low noise, but my actual noise levels aren't great in the end. I think the total BOM cost was ~$150 if you include the PCB and you can get a way faster real scope for that price. It was still a great learning project though. submitted by /u/hapemask [link] [comments]

Three digital multiphase controllers from AOS enable Intel IMVP9.3 Vcore power delivery in high-performance mobile systems. When paired with the company’s DrMOS and Smart Power Stage devices, the AOZ71049QI, AOZ71149QI, and AOZ71146QI form a complete power solution for Intel Panther Lake and Wildcat Lake mobile processor architectures.

The buck controllers use AOS’s advanced transient modulation (A2TM), a hybrid approach that combines digital tuning with analog efficiency. By integrating variable-frequency hysteretic peak current-mode control with advanced phase current sensing, they deliver fast transient response and balanced current sharing across both transient and DC loads. They also maintain low quiescent power across all Intel IMVP9.3 power states, helping maximize battery life in laptops and notebooks. Key features are summarized below:

• Flexible configurations: Up to 4+2+1+2 phase outputs for Core (IA), Graphics (GT), Auxiliary (SA), and LPCORE domains
• Low quiescent current: 5.9 mA at PS0 in 3+2+1+1 configurations
• Power management: Autonomous phase shedding and auto-DCM to reduce power loss
• Compatibility: Supports industry-standard DrMOS and driver + MOSFET power stages from multiple vendors
• Acoustic noise suppression: Integrated features reduce audible noise under dynamic load conditions

The AOZ71049QI, AOZ71149QI and AOZ71146QI are available in production volume, with a lead time of 12 to 16 weeks. Prices start at $2.66 in 1000-piece quantities.

Alpha & Omega Semiconductor 

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Lumissil’s IS32FL3776 matrix LED driver brings expressive intelligent signal displays (ISDs) to software-defined exterior automotive lighting. With 36 constant-current channels providing 60 mA each, it drives dynamic LED light matrices up to 36×6 with as many as 216 individually addressable LEDs.

Automotive ISD systems use matrix LED patterns to communicate vehicle intent, safety status, driver-assistance cues, and brand identity. The IS32FL3776 enables compact LED designs used in RGB mini LED displays, full-width front light strips, grille lamps, automated driving system marker lamps, and other expressive vehicle lighting functions.

The driver features high-resolution, high-frequency dithered PWM for fine brightness adjustment and smooth animations without flicker or camera banding. For improved system efficiency and thermal performance, the IS32FL3776 uses DCFB adaptive control to optimize the LED supply rail while maintaining sufficient headroom for proper current regulation. A software-configurable architecture supports either internal operation or external PMOS drive for power sequencing in larger matrix configurations.

The IS32FL3776 is available for sampling and volume production, with evaluation hardware and reference designs provided to facilitate system development.

IS32FL3776 product page 

Lumissil Microsystems 

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Microchip’s PIC18-Q20 MCUs integrate up to two I3C peripherals and Multi-Voltage I/O (MVIO) in 14- and 20-pin packages as small as 3×3 mm. Well suited for sensor interfacing, real-time control, and connectivity applications, they simplify communication across multiple voltage domains with minimal external circuitry.

Compared to I2C, I3C provides higher data rates and lower power consumption while remaining backward compatible with legacy systems. The MCUs operate across three independent voltage domains, with MVIO-enabled pins supporting I3C communication down to 1.0 V. Additional integration includes a 10-bit ADC with computation, capacitive touch sensing, and an 8-bit signal routing port for flexible peripheral interconnect.

The PIC18-Q20 series can process sensor data, manage low-latency interrupts, and perform system status reporting, reducing the workload on a host MCU in larger systems. These devices are supported by Microchip’s hardware and software development ecosystem, including the PIC18F16Q20 Curiosity Nano Evaluation Kit for rapid prototyping.

Now in production, the PIC18-Q20 MCUs are available from Microchip and its authorized distributors.

PIC18-Q20 product page 

Microchip Technology 

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Toshiba is sampling the TB9M040FTG motor control device, which integrates an MCU and motor driver for controlling small automotive motors. Part of the SmartMCD series, it supports single-channel motor drive currents up to 2 A, enabling direct drive of three-phase brushless DC motors used in electric valves, HVAC dampers, flaps, and grille shutters.

In addition to an Arm Cortex-M23 processor core running at up to 40 MHz and the motor driver, the TB9M040FTG incorporates flash memory, a 5-V high-side driver for power-supply functions, and a power supply that operates at automotive battery voltage levels. It also integrates a LIN transceiver for ECU communication.

The device features a hardware vector engine that offloads field-oriented control (FOC) processing, helping reduce CPU load and software size. Back-EMF detection enables sensorless square-wave control.

All functions are integrated into a compact VQFN36 package, reducing component count in automotive equipment. The TB9M040FTG is compliant with AEC-Q100 Grade 0 and ASIL-B requirements.

TB9M040FTG product page

Toshiba Electronic Devices & Storage 

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The SiFive Performance P570 Gen 3 is a RISC-V out-of-order superscalar vector processor IP designed for scalable performance. SiFive says it delivers a substantial performance improvement over the P550 Gen 1, along with a comprehensive set of mandatory and optional RVA23 profiles.

The IP can serve as the control processor in embedded IoT devices with full networking stacks or as the main applications processor in consumer devices running operating systems such as Android and enterprise-grade Linux. Its vector unit also supports AI model execution and inference on edge devices.

Multicore configurations scale to 16 cores across four clusters with shared L3 and optional L2 cache, a RISC-V-compliant interrupt architecture, and fine-grain power-management control. The P570 supports mandatory RVA23 requirements, including Hypervisor and Vector extensions, along with optional security and management extensions, RISC-V Vector Crypto, and FP16/BF16 capabilities for AI acceleration.

The Performance P570 Gen 3 IP is available now. Visit the product page for configuration and customization details.

P570 Gen 3 product page 

SiFive

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The healthcare industry is progressively moving from a centralized, clinical model to a more patient-centric approach, requiring monitoring solutions that are portable, wearable, and patient-focused. This process involves significant technical and hardware challenges. Designers must find a way to maximize diagnostic accuracy and reliability in a clinical setting while also keeping power consumption, size, and long-term reliability in mind.

The design of a medical device usually follows a modular approach. This means that each part, from the first signal capture to the last communication protocol, must be optimized for speed and efficiency. This article will provide insights into some of the most relevant sensors employed in medical devices as well as the associated technologies, including analog front ends (AFEs), power management devices, and wireless system-on-chips (SoCs) for connectivity.

Pressure sensors

With the wide range of medical devices, from wearable glucose monitors to computerized tomography (CT) scan equipment, there is also a variety of sensors incorporated into these devices. These include pressure and temperature sensors as well as biosensors and accelerometers.

Pressure sensors are used in a wide range of medical equipment, from non-invasive blood pressure monitors to specialized airflow sensors in ventilators. Besides common medical requirements, such as reliability and high sensitivity, these sensors must provide robustness and endurance. Medical-grade pressure sensors must exhibit high linearity and long-term stability, maintaining calibration over weeks or months of continuous operation.

For example, TDK Corporation offers a wide portfolio of piezoresistive pressure sensor dies well-suited for high-precision measurements in the medical sector. Based on advanced silicon MEMS technology, these sensors are grouped into three main categories: absolute, gauge, and differential pressure.

As for the piezoresistive pressure measurement methods, sensor dies are available with frontside and backside absolute measurement and gauge-differential measurement. The frontside configuration, where the electronics are directly exposed, is preferred for dry, non-aggressive gases. The backside design allows the sensor to handle wet media or non-aggressive fluids because the sensitive electronic components are shielded on the opposite side of the pressure-sensing diaphragm. Finally, the gauge configuration is well-suited for physiological measurements relative to ambient.

The C39 series are highly miniaturized dies (with an area of 0.65 × 0.65 mm) with frontside absolute pressure measurement up to 1.2 bar. Able to operate over a temperature range from −40°C to 150°C, these sensors are optimized for high burst pressure and feature narrow sensitivity tolerances and high signal stability. As such, they are suited for integration into high-density wearable medical devices.

Sensors for medical imaging

Imaging technology has made significant advances in the last few years. CT scans used for the diagnosis and monitoring of various conditions, including cancer and cardiovascular diseases, have evolved with the introduction of the photon-counting CT (PCCT).

The main difference between these two techniques lies in how sensors (“detectors”) process X-rays. Conventional CT uses indirect energy-integrating detectors. X-rays hit a scintillator, convert it to light, and then to electricity. In practice, they measure the total energy accumulated, losing individual photon data.

PCCT instead uses direct-conversion sensors that convert X-rays directly into electrical pulses, counting every single photon and measuring its specific energy. This eliminates electronic noise, improves spatial resolution, and allows for precise tissue differentiation at a lower radiation dose.

Ams Osram, now part of Infineon Technologies AG, introduced a system-in-package (SiP) sensor module specifically designed for photon-counting detectors. This sensor, shown in Figure 1, enables a significant reduction in the radiation dose and diagnostic images with higher resolution.

As the company states, the AS5920M module features a 9× reduction of the module’s detector pixel size compared with traditional CT systems. Moreover, more modules can be combined in an array arrangement, increasing the detection area according to the desired CT application.

Figure 1: The AS5920M is a four-sided buttable SiP sensor module engineered for photon-counting detectors (Source: ams Osram)

At the 2025 annual meeting of the American Society for Radiation Oncology, Siemens Healthineers presented the Naeotom Alpha.Prime PCCT scanner (Figure 2) based on cadmium telluride crystal detectors that significantly improve image resolution and contrast. The company introduced the world’s first PCCT scanner in 2021.

Figure 2: Siemens Healthineers’ Naeotom Alpha.Prime PCCT scanner (Source: Siemens Healthineers AG) Embedding AI in sensors

The integration of embedded AI cores directly into biosensors is changing the architecture of medical diagnostics. Previously, devices were limited to a traditional sensing process, wherein all raw data was transmitted to a central processor for analysis. With the direct integration of edge intelligence, sensors can now process data locally, exactly where it is sourced.

The main benefit of this architecture is efficiency, as the device transmits only processed results or alerts. This approach significantly reduces the system’s power consumption, latency, and required bandwidth.

STMicroelectronics has introduced a high-accuracy biosensor that integrates a vertical AFE (vAFE) for biopotential signals (typically cardio and neurological parameters) with a low-power, three-axis accelerometer with AI and anti-aliasing. The ST1VAFE3BX’s vAFE features programmable gain and input impedance and includes a 12-bit ADC.

Providing output data at a rate up to 3,200 Hz, the biosensor is well-suited for biopotential measurement of heart, brain, and muscular activities. The compact size (2 × 2 mm) and reduced power consumption (48.1 µA during normal operation, which can be cut to just 2.6 µA in power-saving mode) suit it for wearables designed for predictive healthcare.

The biosensor features ST’s proprietary machine-learning core (MLC) and finite-state machine (FSM), which allow designers to develop decision-making rules and algorithms to be deployed directly on the chip. The AI-assisted capabilities enable the sensor to autonomously manage motion and activity detection.

This AI feature decreases the interactions with the host controller, reducing the overall power consumption and latency while extending battery life. MLC and FSM can be implemented using ST’s software development tools such as MEMS Studio, which is part of the ST Edge AI Suite.

High-precision AFE

The integrity of a medical device is defined by the quality of its input data. AFEs are components required for interfacing with the human body. They are essential for all types of medical sensors that produce analog signals and therefore require further processing, such as conditioning, amplification, filtering, and digital conversion.

AFEs bridge the gap between physical measurements, typically available in analog form, and the compute device that processes them in digital form. In medical devices, AFEs are required for any sensor that measures physical parameters.

AFEs operate by extracting small-amplitude physiological signals from the environment, which are often noisy or subject to electromagnetic interference. As a result, to achieve medical-grade results, the AFE must provide a high signal-to-noise ratio and low leakage currents.

Among the sensors that require an AFE are biosensors, such as those used in continuous glucose monitoring (CGM) and electrocardiogram patches. Onsemi’s CEM102 is an AFE specifically designed for CGM and similar applications. Based on an amperometric measurement that senses very low currents, the device features a small form factor and low power consumption. These features suit the CEM102 for miniaturized and battery-operated medical devices.

The CEM102 can be operated with a supply voltage ranging from 1.3 to 3.6 V—typically a single 1.5-V silver oxide battery or a standard 3-V coin cell. It supports up to four electrodes, integrates a high-resolution ADC and several DACs for bias setting and a factory-trimmed system, and can be interfaced with a host controller, such as the onsemi RSL15, a secure Bluetooth 5.2 wireless microcontroller (MCU) for connecting to an external device or terminal.

Power management

In the design of compact wearables, such as hearing aids, power management represents one of the most challenging constraints. Designers must select power management integrated circuits (PMICs) with high efficiency, thus preserving the energy provided by small battery cells.

Onsemi’s HPM10 battery-charge controller is a high-performance PMIC engineered to recharge batteries in miniaturized medical devices, typically hearing aids and cochlear implant devices. The device supports different rechargeable battery technologies, including lithium-ion and silver-zinc, and can detect zinc-air and nickel-metal hydride disposable batteries.

The HPM10 also provides a charger communication interface to communicate the state of the charging process to the hearing-aid charger. Other information available on this interface includes the battery voltage levels, current levels, temperature, and battery failures.

Connectivity

A medical device is more effective if it can communicate data to clinicians or electronic health-record systems. Several connectivity protocols are available, and their selection is based on the application’s range and data throughput requirements.

Low-power Bluetooth SoCs are the industry standard for wearables, providing a reliable and efficient link to smartphones or home gateways. For high-bandwidth clinical environments, such as hospitals or clinics, integrating Wi-Fi 6 with Bluetooth Low Energy (LE) represents a suitable connectivity solution.

For example, Silicon Labs’ Series 2 BG29 family of wireless SoCs is designed to provide Bluetooth LE connectivity in an extremely small form factor. The BG29 device’s small size (2.6 × 2.8 mm) suits it for applications such as wearable health and medical devices and battery-operated sensors. The device integrates a DC/DC boost converter supporting a wide voltage range, a Coulomb counter for accurate battery monitoring, 1 MB of flash, 256 kB of RAM, and security features.

Figure 3: Silicon Labs’ BG29 is available in compact QFN and WLCSP packages. (Source: Silicon Laboratories)

NXP Semiconductors is collaborating with Silex Technology, a provider of wireless connectivity and smart edge solutions for the medical and industrial sectors. Silex focuses on wireless solutions for medical applications requiring high longevity, cybersecurity features, and high reliability. Patient monitors, medical wearables, and other connected devices often operate in hospitals where several Wi-Fi access points are available.

Silex integrates NXP’s Wi-Fi SoCs in its Wi-Fi 6 + Bluetooth 5.3 and 5.4 module solutions, including NXP’s IW611 Wi-Fi 6 SoC and RW610 Wi-Fi 6 wireless MCU.

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It’s always gratifying when a simple and successful design idea luckily turns out to have additional applications that you didn’t originally envision.  Here’s an example.

Wow the engineering world with your unique design: Design Ideas Submission Guide

A while back, the design shown in Figure 1 was accepted for publication:

Figure 1 U1 plus R1 through R5 current steering networks convert a 0/20mA input into a 4/20mA output.

Later, the same circuit, when wired up differently as shown in Figure 2, turned out to be an equally good fit in a different job:

Figure 2 This 4/20mA current loop converter integrates an OFF/ON field contact.

A recent Design Idea by another frequent contributor, Jayapal Ramalingam, addressed the problem of convenient calibration of precision current loop receivers in industrial applications. His design comprises a linear control input that expedites calibration and testing.   He explains that it helps to:

…”calibrate the analog input modules of distributed control systems (DCSs) and programmable logic controllers (PLCs) by simulating process signals.”…

This inspired me to wonder if a different approach to the same calibration problem might also be useful.  I imagined a design in which the three standard analog test current loop levels: 0, 4mA, and 20mA, were accurately preset and quickly accessed by flipping a switch. I then proceeded to ponder whether that same friendly little converter circuit might work in such an application.

Figure 3 shows the result:

Figure 3 The three-position, center-off, DPDT switch S1 converts this current converter (verbiage redundancy pun-intentional) into a convenient current calibrator.

Not only did it fit, but the calibration procedure for the new role is just as quick, simple, and easy to accomplish in a single pass as it was before.

1. Set S1 to the 4mA position.
2. Tweak 4mA adj for 4mA output (as measured, for example, with a precision DMM).
3. Set S1 to the 20mA position.
4. Adjust 20mA adj for 20mA output (ditto).

So, it turns out that the same circuit thriftily fits three related, yet different, applications – a triple-duty design trifecta.

Stephen Woodward‘s relationship with EDN’s DI column goes back quite a long way. Over 200 submissions have been accepted since his first contribution back in 1974.  They have included best Design Idea of the year in 1974 and 2001.

Related Content 

• Silly simple precision 0/20mA to 4/20mA converter
• Double-duty current loop transmitter
• Linearly variable two-wire loop current generator
• Full circle current loops: 4mA-20mA to 0mA-20mA
• 4/20mA to 0/20mA loop current converter for grounded loads

The post Triple-duty current loop calibrator appeared first on EDN.

Regardless of the applications, most artificial intelligence (AI) chip designers face the same challenges. Whether it’s cloud data centers, edge devices, automotive platforms, or industrial robotics, optimal performance now depends on how efficiently data is moved.

When data movement is delayed, even the fastest compute engines are left waiting, reducing throughput, increasing latency, and wasting power.

As AI designs continue to grow in complexity, managing massive data flows through fixed, point-to-point connections no longer scales efficiently. Designers are now dealing with hundreds of compute engines and memory instances, each with different performance requirements, all of which must move data simultaneously.

A network-on-chip (NoC) brings order to chaos by providing a scalable, shared communication infrastructure that moves data where it needs to go with controlled latency and bandwidth. With built-in mechanisms for congestion management, traffic prioritization, and workload isolation, NoCs help teams deliver consistent, predictable performance while staying within tight power, area, and timing budgets.

Different markets, same bottleneck

Whether in hyperscale cloud infrastructure or inside an embedded vision processor, the core problem is data bottlenecks. The end markets differ, but the underlying architectural constraint remains the same. In the cloud, the goal is maximum throughput. Training clusters push bandwidth into the terabytes-per-second range. Massive GPUs and AI accelerators continuously ingest and process vast datasets. In large data center GPUs, more than 80% of dynamic energy is consumed by data transfers to and from DRAM. That energy is not spent on computing. It is spent moving bits.

At the edge, priorities flip. Systems such as autonomous vehicles, robotics, and smart cameras demand microsecond-level latency, strict determinism, and ultra-low power consumption. Edge AI devices may spend up to 90% of inference time waiting on memory I/O.

This is the invisible drain on AI performance.

Why NoC architecture matters

The NoC is the backbone that determines how efficiently data flows within a system-on-chip (SoC) or across multiple dies. However, the NoC must be optimized correctly. If not, the entire system slows down, regardless of how powerful the compute cores may be.

AI designs often rely on wide parallel interfaces between IP blocks. As system innovation increases, routing congestion, timing closure issues, and power overhead become more difficult to manage. An NoC addresses these challenges by packetizing traffic. Transactions are broken into packets and routed across a structured fabric, much like off-chip networking. This approach significantly reduces wiring complexity.

A wide AXI interface can require hundreds of signals; for example, a given AXI bus interface that requires 280 signals can be reduced to 150 by packetizing transactions. Fewer wires mean less congestion, simpler routing, easier timing closure, reduced silicon area, and lower dynamic power, as shown in the figure below.

Here is an outline of the advantages of packetized data with NoC IP Source: Arteris

Equally important, an NoC decouples IP blocks from transport details. Designers integrate heterogeneous CPUs, GPUs, NPUs, memory controllers, and accelerators without manually wiring hundreds of signals between blocks. The network fabric handles transport abstraction. This level of decoupling does more than simplify integration within a single die. It also lays the groundwork for the next major shift in system design, where functionality is distributed across multiple dies and coordinated at the system level.

From monolithic dies to systems of systems

The separation of IP from transport becomes critical as designs transition to chiplet-based architectures. The shift enables teams to optimize each piece of silicon independently for its specific function and power trade-offs. It also improves yield, lowers costs, and makes it easier to increase compute capacity by adding or reusing chiplets as requirements change.

Within each die, a coherent NoC uses standard protocols such as AMBA CHI or ACE. Non-coherent fabrics connect peripherals and specialized engines into the broader system. Across dies, UCIe enables high-speed die-to-die communication. In advanced multi-package systems, coherent and non-coherent NoCs communicate seamlessly across chiplet boundaries.

The result is effectively a system of systems, with multiple specialized silicon components orchestrated into a unified compute engine. The NoC fabric spans the entire package, coordinating traffic between dies and subsystems.

In this environment, the interconnect is no longer just a supporting block. It shapes the entire system architecture. Every AI system, whether in the cloud or at the edge, has to strike the right balance among three things. Bandwidth must keep GPUs, XPUs, and AI engines fully utilized. Latency must remain low to support real-time inference and control. Efficiency must hold power and thermal budgets within limits as systems expand.

Designers also need a practical way to grow compute resources without redesigning the interconnect. Modular tiling approaches address that need. Each tile includes its own network interface unit and can be replicated across an NPU array. Need more compute? Add more tiles. The fabric scales without requiring a complete redesign.

Closing the architectural loop

In AI SoCs, designing the NoC requires more than defining the logical topology. Engineers should introduce physical awareness early in the design process. That means using floorplan information, estimated wire distances, and timing constraints. Physical awareness must be built directly into the design flow.

A modern NoC design flow includes:

1. High-level architectural modeling and simulation
2. Integration of physical constraints through virtual floor planning
3. Automatic insertion of pipeline stages with built-in timing analysis
4. Closed-loop export of constraints to physical synthesis tools

This approach bridges the gap between architectural intent and layout reality. In production designs, physically aware NoC automation has demonstrated the ability to reduce total wire length by roughly 26%, cut maximum latency by half, and improve overall productivity by an order of magnitude. Tasks that once required weeks of manual tuning can now be completed in less than a day.

Cache hierarchy and data locality

Interconnect optimization must be paired with effective cache architecture. Multi-level cache hierarchies, including L1, L2, and L3, store frequently used data close to the compute engines, reducing memory access latency. Without an effective cache hierarchy, CPU utilization can drop to single digits.

In some AI SoC regions, last-level non-coherent caches improve data availability without participating in a full coherency protocol. Workloads that do not require tight synchronization, such as certain signal-processing or multimedia tasks, benefit from this approach, which simplifies the design while improving throughput. By increasing data locality, the cache structure reduces reliance on external memory and stabilizes interconnect traffic.

The reality of AI SoC design

The cost of developing leading-edge SoCs has risen from under $100 million a decade ago to more than $700 million today. So, each design iteration or silicon re-spin carries enormous financial risk.

Manual integration processes, fragile scripting, and misaligned hardware-software interfaces amplify that risk. Automated SoC integration flows that validate IP early, maintain consistent specifications across teams, and compile millions of registers in minutes can significantly reduce development time and errors.

Arteris addresses these architectural demands with interconnect IP purpose-built for complex AI platforms where efficient data transport determines overall system behavior. Its FlexNoC and Ncore solutions provide configurable non-coherent and coherent fabrics that support heterogeneous compute clusters and multi-die designs, reducing communication bottlenecks that limit utilization.

By aligning scalable interconnect architecture with disciplined implementation methodology, these interconnect solutions enables design teams to translate system intent into silicon more predictably in an era defined by rising complexity and cost sensitivity.

Automation and physically aware design are no longer optional optimizations. They are survival tools in the AI decade.

Andy Nightingale, VP of product management and marketing at Arteris, has over 39 years of experience in the high-tech industry, including 23 years in various engineering and product management roles at Arm.

 

Related Content

• Who’s Who in AI SoCs
• Why NoC tiling matters in AI-centric SoC designs
• SoCs Get a Helping Hand from AI Platform FlexGen
• Speeding AI SoC development with NoC-enabled tiling
• How AI is changing the game for high-performance SoC designs

The post How data movement defines performance for AI silicon appeared first on EDN.

Defence technology company Tonbo Imaging receives an award and a contract from the Indian Navy under the ADITI 3.0 innovation framework to integrate and commission a High Power Microwave (HPM) system for naval platforms. The programme supports iDEX and the Defence Innovation Organisation (DIO) under the Ministry of Defence, Government of India. Within the scope of the engagement, Tonbo Imaging will undertake system integration and commissioning activities, followed by the supply of multiple production units upon successful development, validation, and acceptance.

High-power microwave systems represent a strategically significant direct-energy capability and are considered a strategic asset; only a limited number of countries possess them today. Such systems provide a non-kinetic means of disabling or degrading adversary electronics, sensors, and unmanned systems, and one of the few practical approaches to countering swarms of drones, making them increasingly relevant in modern maritime and asymmetric threat environments. The Indian Navy’s continued investment in this domain reflects a forward-looking approach to electromagnetic spectrum dominance and next-generation deterrence.

ADITI (Advanced Defence Technology Incubation) is a Government of India initiative to enable the maturation, integration, and validation of advance defence technologies before induction. The selection of Tonbo Imaging under ADITI 3.0 reflects the emphasis on indigenously developing strategic capabilities aligned with the Navy’s evolving operational requirements and long-term force modernisation plans.

Commenting on the development, Arvind Lakshmikumar, Managing Director and Chief Executive Officer, Tonbo Imaging India Limited, said, “This programme represents a significant responsibility to execute complex capability integration with discipline, rigour, and clear alignment to end-user operational needs. Over the past several years, Tonbo Imaging has invested substantially in the indigenous development of core building blocks of High Power Microwave technology, including critical sub-systems and vacuum tube sources. We are among the very few private organisations to own core intellectual property in vacuum-tube technologies that are fundamental to HPM systems, and this deep technology foundation has been a key factor in our selection for this naval programme. For the class of effects required in High Power Microwave applications, vacuum tube–based sources remain the practical path forward, as they can generate the extremely high peak power and energy levels necessary for effective target coupling. Solid-state RF sources, while well suited for many RF applications, cannot today achieve the required peak power and pulse energy levels within feasible size, weight, and efficiency envelopes for operational HPM systems.”

With this engagement, Tonbo Imaging’s role extends well beyond that of an imaging and electro-optics company, reinforcing its position as a defence technology company focused on the development and integration of advance defence systems. The programme underscores the company’s growing involvement in complex system-level integration, advanced electronics, embedded software, and emerging direct-energy and mission systems, in addition to its prevailing strengths in electro-optics. This evolution reflects Tonbo Imaging’s transition toward delivering integrated defence capabilities.

About Tonbo Imaging India Limited

Tonbo Imaging India Limited is a defence technology company that focuses on the design, development, and integration of advanced sensing, perception, and mission-critical systems for military and security applications. The company’s portfolio spans electro-optics, thermal imaging, situational awareness, advanced electronics, embedded software, and emerging directed-energy technologies, enabling the delivery of integrated defence solutions that support operational requirements across land, maritime, and air domains. Tonbo Imaging continues to evolve as a defence technology and systems company, investing in the development of next-generation directed-energy systems as well as advanced defence solutions such as loitering munitions and counter-unmanned aerial systems (C-UAS), reflecting its strategic focus on addressing emerging operational threats through indigenous capability development.

The post Indian Navy awards ADITI 3.0 contract for High Power Microwave System to Tonbo Imaging appeared first on ELE Times.

submitted by /u/AltCtrlGraphene [link] [comments]

The Sensors Converge 2026 conference showcased some of the latest advances in sensor and sensing solutions for applications ranging from wearables and smartphones to industrial and automotive. The show, with over 160 exhibitors, also highlighted the industry’s shifting focus to edge AI and smart, connected systems with demos that showcased real-world applications in edge AI, robotics, and autonomous systems.

While sensor manufacturers continue to focus on shrinking solutions and package sizes, this year’s product introductions also indicate an increased need for lower power consumption. Here is a sampling of new sensors featured at this year’s show.

Vibration sensors across wearables and industrial

Upbeat Technology showcased its latest family of low-power MEMS vibration sensors and vibration processing units (VPUs), including the UPM01 and UPM02 series with a UP201/301 dual-core RISC-V AI microcontroller (MCU), aimed at high-quality voice clarity and predictive intelligence in a small footprint.

Suited for space-constrained wearables applications, the UPM01/UPM02 VPU, also called a bone-conduction microphone, measures 3.2 × 2.5 mm, and the UP201 dual-core RISC-V AI MCU measures 3.0 × 3.0 mm. Together, they create Upbeat’s Tiny AI Engine that provides on-device intelligence to wearables, industrial systems, drones, and consumer electronics. The solution enables “crystal-clear voice” in open wearable stereo (OWS) headsets, smart glasses, and intelligent voice recorders and delivers predictive maintenance for industrial automation.

The UPM01 series offers multiple interface variants: the UPM01A (analog), UPM01Ax (higher-sensitivity analog), UPM01D (digital), and UPM01Dx (higher-sensitivity digital). The UPM02 provides analog and digital options with a higher signal-to-noise ratio (SNR) for applications in which audio clarity is critical, the company said.

The UPM01 extends the frequency response of conventional MEMS vibration sensors from 5 Hz to 11.3 kHz and delivers an SNR of 60 dB(A) for a more accurate sound capture, while the UPM02 offers a frequency response range from 5 Hz to 5.4 kHz and an exceptionally high SNR of up to 68 dB(A).

Both series consume minimal power and can operate for extended periods on a single battery charge, making them suited for mobile devices, wearables, and other battery-powered applications.

The UP201/UP301 heterogeneous dual-core RISC-V edge AI platform targets energy-efficient deep-learning applications, enabling AI analysis closer to the data source for fast response and lower bandwidth usage. Delivering ultra-low-power, always-on intelligence, the platform enables continuous sensing with minimal power and instant wake-up for intensive AI tasks.

Mass-production shipments for the UPM01/UPM02 have started, with the UP201/UP301 scheduled to ship in October 2026.

Upbeat also unveiled its UP301 + UPM01 Falcon Demo Kit, described as a ready-to-run evaluation platform for machine-vibration analysis. Aimed at engineers who want to prototype and validate predictive maintenance solutions, the kit includes a UP201 dual-core RISC-V AI MCU EVB, variable-speed motor, two UPM01D FPCs, power adapter, and access to the Falcon graphical user interface (GUI), the Upbeat Vibration Analysis Suite GUI software. The demo kit is available for purchase at www.upbeattechtw.com/products/demo-kits.

Other demonstrations included OWS headsets, smart glasses with AI voice interaction, a smart AI voice recorder, a factory machine-vibration application, and smart AI toys with touch-gesture recognition.

Upbeat’s UPM01, UPM02, and UP201 devices create its Tiny AI Engine. (Source: Upbeat Technology)

Ahead of the show, STMicroelectronics announced its wide-bandwidth, three-axis vibration sensor, aimed at saving space and energy in industrial and automotive condition-monitoring applications. With an extended temperature range of −40°C to 125°C, the IIS3DWBG1 enables vibration monitoring in harsh environments.

The IIS3DWBG1 offers a selectable, full-scale acceleration range of ±2/±4/±8/±16 g and can measure accelerations with a bandwidth up to 6 kHz with an output data rate of 26.7 kHz. Housed in a 2.5 × 3-mm LGA-14L package, the MEMS sensor is suitable for industrial condition-monitoring systems, in which sensor placement and mounting are critical to measurement accuracy.

The small size and wide operating temperature range allow the flexibility to place small, externally attached sensors at optimal diagnostic locations while enabling integration inside smart motors and smart gearboxes, ST said.

In addition, the low power consumption delivers long-lasting operation in battery-powered applications. The sensor’s wide bandwidth and high resolution simplify capturing patterns associated with defects or wear, as well as equipment setup issues such as looseness and misalignment.

The IIS3DWBG1 can also detect electromechanical vibrations in coils, transformers, snubber capacitors, busbars, connectors, and general vibrations originating in the power electronics module, such as traction inverters. This enables automotive OEMs to extend remote diagnostics to cover power modules, as well as traction inverters in electric vehicles.

Thanks to a flat frequency response from DC to above 6 kHz (−3 dB point) and noise density of 75 µg/√Hz in three-axis mode, the sensor detects extremely small vibrations, providing enhanced early warning to prevent equipment failures. The sensor is highly resistant to mechanical shocks, according to ST, and integrates digital features including a configurable low-pass or high-pass filter with selectable cutoff frequency, an embedded FIFO, interrupts, a temperature sensor, and self-test capability.

The IIS3DWBG1 is in production now. An evaluation kit is available.

The ST IIS3DWBG1 MEMS vibration sensor can operate in harsh automotive and industrial applications. (Source: STMicroelectronics) AMR and TMR sensors

Murata Manufacturing Co. Ltd. introduced its ultra-low-power anisotropic magnetoresistance (AMR) sensors, the MRMS166R and MRMS168R. These sensors are designed to increase battery life in healthcare, wearable, and IoT devices. The MRMS166R is claimed as the first AMR sensor to combine an average current consumption of 20 nA with operation from a 1.2-V supply, enabling extended battery life in coin-cell-powered systems.

These solid-state magnetic sensors detect the presence or absence of a magnetic field and generate an output signal that system logic uses to control functions such as transitions between active and sleep modes. This provides contactless switching without mechanical components, improved reliability, and support for sealed, miniaturized designs, Murata said.

This automatic switching between active and sleep modes is widely used in battery-powered devices to reduce standby power consumption and extend operating life, Murata said. Applications include healthcare, such as capsule endoscopes and medical patches; wearable devices, including AR glasses and wireless earbuds; and security-related IoT devices, such as door-open/close-detection systems and smart locks.

These devices commonly use silver oxide coin batteries (typically 1.55 V) that place constraints on available capacity and operating voltage. This means AMR sensors used as magnetic switches must minimize current consumption while maintaining stable operation at a low voltage, Murata said.

To address these challenges, Murata redesigned the AMR sensor’s internal circuitry, enabling ultra-low current consumption and operation down to 1.2 V. This significantly reduces battery consumption during standby operation, supporting device operation for more than two years in typical use.

The MRMS166R operates over a 1.2-V to 3.6-V supply range (1.5 V typ.) with an average current consumption of 20 nA and a maximum current output of 1 mA. The MRMS168R operates over a 2.0-V to 3.6-V supply range (3.0 V typ.), with an average current consumption of 80 nA and a maximum output current of 12 mA, providing higher output drive capability for devices requiring increased load current. Both devices are housed in a compact package measuring 1.0 × 1.0 × 0.4 mm (0.04 × 0.04 × 0.02 inches). The MRMS166R and MRMS168R sensors are now in mass production.

Murata’s MRMS166R/MRMS168R AMR sensor (Source: Murata Manufacturing Co. Ltd.)

MultiDimension Technology Co. Ltd. debuted its tunneling magnetoresistance (TMR) TMR2531 (±1,000-Gauss linear range) and TMR2539 linear sensors (extended ±1,500-Gauss linear range) for smartphone cameras at Sensors Converge. Available in production quantities, these ultra-compact TMR linear sensors are designed for high-precision smartphone optical image stabilization (OIS) applications.

These sensors enable micron-level displacement measurement in voice coil motor (VCM) modules, allowing VCM driver ICs to precisely correct camera shake in real time during photo and video capture, MDT said. They measure the z-axis perpendicular magnetic field amplitude via a Wheatstone full-bridge configuration with four high-SNR TMR elements.

Periscope-style telephoto lenses have pushed OIS precision requirements into the micron scale to control prism positioning over extended motion ranges, MDT said. The new TMR sensor technology addresses these challenges with a high SNR, broad linear measurement ranges, and high immunity to magnetic interference, making it suited for advanced camera autofocus and OIS solutions in flagship smartphones.

Both series offer a 1.0-V to 5.5-V supply voltage and a shielding capability of ±3,000 Gauss for stable operation in interference-prone VCM environments. They are housed in a small DFN4L package (0.8 × 0.5 × 0.25 mm) for constrained VCM designs.

Faster sensor development

TDK Corp. introduced two development tools at Sensors Converge to simplify evaluation of TDK sensors. The InvenSense SensorStage software is an evaluation platform to simplify development and accelerate data analytics for TDK’s SmartMotion inertial measurement units (IMUs) and TMR magnetometers, while SensorGPT uses AI to generate simulated datasets to improve and accelerate development of edge AI IoT devices.

The all-in-one platform SensorStage bridges the gap between simple GUIs and custom test benches, offering advanced visual analytics and automated scripting to help engineers move from setup to insight without manual configuration, TDK said. SensorStage enables evaluation of complex, on-chip algorithms for applications in OIS, wearables, AR/smart glasses, and IoT with a future-proof architecture that supports existing and upcoming high-performance sensors.

The SensorStage platform is paired with the SmartMotion development board. Together, sophisticated on-chip features including machine-learning algorithms, the APEX engine for Gyro Assisted Fusion, motion and event detection, and chip-level power consumption are visualized. This delivers precise calibration and faster time to market for complex designs.

SensorStage is currently available for InvenSense ICM-456xx and ICM-426xx SmartMotion IMUs and will soon be available for additional InvenSense MEMS sensor solutions.

TDK’s SensorStage platform pairs with the SmartMotion development board. (Source: TDK Corp.)

SensorGPT uses generative AI, signal processing, statistical methods, and simulations to create and manage sensor data at scale. Particularly aimed at smart IoT and ambient IoT applications, the AI tool streamlines model development and deployment, reducing time and cost, while enhancing the performance and efficiency of edge AI models and applications, TDK said.

SensorGPT sensor data synthesis trains generative models with limited real-world data to learn underlying patterns and generates synthetic data that mimics real-world data. It reduces the reliance on real-world data through intelligent sensor data synthesis, cutting data-collection efforts from 80% to nearly 10%, according to TDK, which enables faster, more scalable edge AI development.

The AI tool leverages physics-based and mathematical models to simulate and generate synthetic sensor data and uses mathematical and computational techniques to simulate data reflecting the dynamics and characteristics of real sensor outputs, TDK explained.

Other features include data-augmentation techniques that automatically transform existing sensor data into diverse datasets spanning a range of conditions and scenarios, while the assisted annotation streamlines the labeling of training data, which improves the quality for model training.

SensorGPT generates a 90% similarity between synthetic and real-world sensor data. This enables the use of the synthetically generated data for faster edge AI solution prototyping, testing, and deployment. It reduces edge AI model-building time from five-plus months down to a few weeks, according to TDK.

Generated dataset in a vibration sensor demo using TDK’s SensorGPT (Source: TDK Corp.)

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