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In late June, Tokyo-based Mitsubishi Electric Corp will begin sequentially shipping samples of two types of new 5th-generation silicon carbide metal-oxide-semiconductor field-effect transistors (SiC MOSFETs) in bare die form...

Finally! I made a perfect pcb on a CNC with a UV mask 🔋. I really love how the copper looks under the mask. submitted by /u/Big_Lack_ [link] [comments]

Благодійна платформа КПІ ім. Ігоря Сікорського kpi чт, 06/04/2026 - 16:00

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Clock speeds get faster. Per-cycle (and per-clock edge) address and data dollops get larger. And protocols get more efficient. But here we’re talking about external, not internal, buses.

Back in 2023, I devoted two blog posts’ worth of content to comparing various memory card technologies, products and speed bin options, initially in March (identifying a fake card in the process) and more in-depth in July. Since then, I’ve come across numerous examples of both evolutionary and revolutionary successors to the devices discussed in that two-part series, not to mention those covered in even more distant-past writeups (themed, for example, around the cameras, digital audio recorders and other devices that leverage such storage).

I’ve had this follow-up piece in my to-do list for a while now, and I’ve finally decided to actualize my longstanding aspiration before the dust pile accumulating on this specific list entry gets any deeper. Not every technology to be discussed in the paragraphs to follow will likely achieve high-volume market success, mind you, with any sooner-or-later failures not necessarily the result of implementation shortcomings, either. Note, for example, that today’s (and past) industry supply constraints encourage manufacturers to “double down” on maximizing the output and profitability of existing approaches, versus devoting scarce capacity to dubious bets.

That said, win or lose there’s usually an interesting story behind each approach. Without further ado…and with the upfront qualifier that I’ll be intentionally delaying any discussion of USB-interface memory devices until later, since their connector locations compel them to be fully external to the system, either sticking straight out of it or cable-tethered to it…and that for related reasons, I won’t be covering eMMC and other fully internal formats, either…and lastly, that I’ll be skipping over legacy formats that were proprietary and/or otherwise non-impactful…

Historical precedents

A short writeup, “History Repeating” at Virginia Tech’s website, begins as follows:

Variations on the repeating-history theme appear alongside debates about attribution. Irish statesman Edmund Burke is often misquoted as having said, “Those who don’t know history are destined to repeat it.” Spanish philosopher George Santayana is credited with the aphorism, “Those who cannot remember the past are condemned to repeat it,” while British statesman Winston Churchill wrote, “Those that fail to learn from history are doomed to repeat it.”

Long-time readers may recall that I’ve referenced variants of this same quote theme in several past writeups, consistently with a negative connotation involving the downsides of ignorance to the past. That said, excessive dependence on history lessons can also be problematic, resulting in evolutionary, overly constraining baby-steps that suppress alternative more revolutionary strides, which may lead to failure but may also dramatically leap beyond traditional approaches.

I’ll leave you to decide for yourselves what to conclude from this first case study, admittedly too personal to likely allow me to be completely arms-length about it! Embedded within the tuple (card identifier) data structures reported by Intel’s Series 2 flash memory cards were the initials of the small team of developers, myself among them, who designed their ASIC (30 years ago…yikes!). I subsequently led the technical marketing launch of the 28F008SA 8 Mbit flash memories inside those same cards, followed by the definition, development and introduction of 16 and 32 Mbit component successors and cards based on them, all in the early-to-mid-1990s.

Products such as these, representing the industry’s first removable and high capacity (for the era, at least) memory cards, added these tuple structures and other enhancements in order to deliver full Personal Computer Memory Card International Association (PCMCIA, later known as PC Card) compatibility, in contrast to Series 1 precursors which were more elementary multi-component arrays along with address decode and chip select logic. Intel’s and others’ similar products were specifically referred to as linear flash memory PC Cards, both to differentiate them from other PCMCIA card types—modems, ISDN and SCSI, for example, and living on (at least to a degree) with CableCARDs—and from alternative ATA-interface flash memory cards.

The key difference between the two memory card types centered on where the flash media management intelligence was located: in the card itself for ATA flash PC Cards, thereby presenting a standardized hardware and software interface to the system regardless of what (and whose) media was inside, versus in the system, implemented as software and/or dedicated hardware, for the linear flash PC card approach. Proponents of the latter scheme touted its claimed reduced media bill-of-materials cost, not to mention the potential ability to direct-execute code out of it (acting as a big parallel-interface chip), but it was inherently relevant for only NOR (vs NAND) memory suppliers, along with being a “heavier lift” for system developers. For these and other reasons, the ATA approach eventually won out in the marketplace.

Miniaturization

That said, Intel and several of its NOR flash memory partner/competitors had also taken a stab at miniaturizing the linear flash PC Card with the creatively named (ha!) Miniature Card format:

Other flash memory suppliers countered with the ultimately much more popular CompactFlash card, now maintained by the aptly named CompactFlash Association (CFA), whose hardware interface was similarly PCMCIA-derived albeit instead (as with the ATA flash PC Card precursor) focused on the IDE/ATA (and later, UDMA) command set:

Amid this “where is the media management intelligence best located” debate, two other notable contending approaches of the same timeframe also bear mentioning. The first, SmartMedia, was championed by Toshiba (as well as, later, by its primary competitor, Samsung):

SmartMedia was essentially a single (although a few variants embedded multiple) NAND flash memory die embedded within a thin plastic membrane, plus a multi-contact metallic interface that wirebond-direct-connected to the die with no intervening media controller intelligence.

Conceptually sounds like linear flash PC Cards and their derivatives, doesn’t it? Yes…and no. For one thing, SmartMedia was much smaller than either Miniature Card or Compact Flash. For another, it was based on NAND flash memory, which was more HDD-like in its core attributes  (notably erase block size and speed) than NOR, simplifying system-side media management development. And then there was the fact that Toshiba wasn’t just a semiconductor supplier; its various systems divisions were potential SmartMedia implementers, and the company also did a good job of cultivating business from other Japanese and broader Asian systems manufacturers.

Finally, near the end of the last century (in 1997, to be exact), Sandisk and systems partners Siemens and Nokia unveiled the MultiMediaCard (MMC), which ultimately came in multiple dimension options, as well as in both standard and clock-boosted performance variants:

MMC is best known today in its aforementioned non-removable eMMC form, which itself is being slowly supplanted by the embedded variant of the MIPI- and SCSI-based Universal Flash Storage (UFS) (an organization whose own removable-version standard ironically has conversely been underwhelmingly adopted by the industry). Today’s generational successor to MMC is the Secure Digital (SD) card, originally referred to as SecureMMC:

which built on the MMC foundation with “enhancements including a digital rights management (DRM) feature, a more durable physical casing, and a mechanical write-protect switch.” The SD standard’s successive iterations have expanded the available clock speed, protocol and electrical contact count options in a backwards-compatible fashion to keep pace with flash memory performance gains, such as in this high-end V90 card from OWC:

The microSD Card derivative tackled substantive dimensional decreases with notable success; here’s one alongside the SmartMedia card I showed you earlier:

One interesting newer SD (and microSD) card specification variation that I became aware of recently when shopping for storage media for a couple of new Raspberry Pi cards is the Application Performance Class. Quoting from Kingston Technology documentation:

A new classification has been presented with the introduction of Android’s Adopted Storage Device feature. The App Performance Class assures minimum random and sequential performance speeds to meet both run and store execution time requirements under given conditions. It does this simultaneously while providing storage for pictures, videos, music, files and other important data. Basically, they’re ideal for use in smartphones and mobile gaming devices that run applications at random read and write speeds while also being used for storage.

 There are two ratings for the App Performance Class which are known as A1 and A2. A1 has a minimum random read of 1500 IOPS and a minimum random write of 500 IOPS while A2 has a minimum random read of 4000 IOPS and a minimum random write of 2000 IOPS. Both A1 and A2 have a minimum sustained write speed of 10MB/s. The App Performance Class is something to consider [editor: for example] when planning on installing Android apps on a microSD card.

And, by the way, unlike the SmartMedia competitor of the day, both MMC and successor SD Cards notably also embed (despite their smaller sizes) media management intelligence that simplifies and standardizes the system implementation. Moore’s Law strikes again, eh?

Hang tight; I’ll be right back

Believe it or not, I originally envisioned this being, and wrote it as, a single unified blog post. However, as thought of more (and more…and more…) things to include, the wordcount grew (and grew…and grew…), transforming it into something resembling a small book (I exaggerate, but you get my drift). Having passed through 1,500 words at the beginning of this paragraph, I’m instead going to pause for now, intending (God willing) to share the other half of this now-two-part series with you next week. Until then, please share in the comments your thoughts on what I’ve covered so far!

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

Related Content

• Memory cards: Specifications and (more) deceptions
• SD card speeds: question your assumptions
• AI boom and the politics of HBM memory chips

The post Memory card interfaces keep pace with the internal bus evolution race: Part 1 appeared first on EDN.

US-based Raytheon (a business of aerospace & defense company RTX of Arlington, VA) has been awarded a $515m contract from the US Navy for the SPY-6 family of radars. The contract is a follow-on to the Integration and Production Support contract, awarded in June 2025, and includes upgrading Flight IIA destroyers with the SPY-6(V)4 variant...

As the rapid expansion of AI data centers and the intensifying race for AI computing power are accelerating the transition toward transmission speeds above 1.6Tbps, major players such as NVIDIA, Google and Meta are securing a stable supply by strategically locking in production capacity from suppliers of electro-absorption modulated lasers (EMLs) and continuous-wave distribute feedback laser diodes (CW-DFB LDs), notes market research firm TrendForce...

Murata Manufacturing Co., Ltd. introduces the GCJ21BD72A225KE02, a soft-termination chip multilayer ceramic capacitor (MLCC) for automotive powertrain and safety equipment. This world’s first soft-termination chip MLCC product achieves the highest available capacitance of 2.2μF at 100Vdc in the smallest 0805-inch (2.0×1.25mm) size.

As vehicle electrification accelerates and autonomous driving (AD) and advanced driver-assistance systems (ADAS) grow more sophisticated, engineers face increasing pressure to pack more functionality into tighter board spaces. The wider adoption of 48V power systems further demands components that combine high capacitance, high voltage tolerance, and a small footprint. At the same time, mechanical stress from board flexure, due to vibration and thermal cycling while driving, remains a reliability concern. The GCJ21BD72A225KE02 addresses all these challenges.

Built on Murata’s proprietary ceramic material design, including fine particle size and uniformity control, the soft-termination chip MLCC achieves 2.2μF at 100Vdc in the 0805-inch size, a rating previously only possible in the larger 1206-inch (3.2×1.6mm) size. The result is an approximately 51% reduction in board mounting area compared to Murata’s previous 2.2μF/100Vdc offering, and an approximately 2.2x increase in capacitance over its previous 0805-inch, 100Vdc product. Soft termination further enhances field reliability by absorbing board flexure stress and reducing post-mount cracking.

The GCJ21BD72A225KE02 supports an operating temperature range of -55°C to +125°C and meets X7T temperature characteristics per EIA standards. Murata will continue expanding its automotive-grade MLCC lineup, delivering the miniaturization, high capacitance, high voltage ratings, and reliability that next-generation vehicles demand.

The post Murata Introduces World’s First 2.2μF/100Vdc Soft-Termination Chip MLCC in 0805-inch Size for Automotive Applications appeared first on ELE Times.

ROHM says that its 750V silicon carbide (SiC) MOSFET has been adopted in a BBU (battery backup unit) for AI server power supplies. With the rise of generative AI, AI server power systems are shifting to higher voltages and rapidly transitioning to HVDC (high-voltage direct current) architectures. In this environment, ROHM’s device was selected as a SiC power device that supports next-generation power supply systems...

Indium Corp of Clinton, NY, USA (a supplier of refined gallium, germanium, indium and other specialty technology metals) and Ames National Laboratory have announced an R&D partnership to expand US production of gallium. The new alliance will focus on developing the technologies needed to establish a domestic supply chain for an element that currently relies almost exclusively on imported sources...

Qorvo, a leading global provider of connectivity and power solutions, announces a new portfolio of silicon-on-insulator (SOI) RF switches and digital step attenuators (DSAs) for defense, aerospace, and infrastructure customers. This new portfolio simplifies RF system design, reduces BOM complexity, and accelerates integration in wideband systems.

These new solutions address growing system demands for broader frequency coverage, agile signal routing, and optimal integration without the complexity of legacy GaAs-based RF control component approaches or multi-vendor RF control chains. With TTL-compatible control that eliminates the need for a negative voltage rail, Qorvo’s SOI portfolio helps designers simplify biasing networks, reduce BOM count, streamline board layouts while maintaining the fast-switching speeds, high isolation, and high linearity required in defense and aerospace applications. The portfolio gives designers a simpler alternative to legacy RF control approaches that require negative bias rails, multiple control components, and more complex board-level integration.

“Customers are looking for ways to simplify RF control architectures without sacrificing the switching speed and RF performance required in modern defense systems,” said Doug Bostrom, general manager of Qorvo’s Defense and Aerospace business. “By eliminating the need for a negative voltage rail, our SOI portfolio helps reduce design complexity, streamline integration, and provide a faster path from design to deployment.”

Product	Function	Frequency Range	Key Differentiators	Target Applications
QPC2320	Reflective SPDT Switch	Up to 15 GHz	Low insertion loss, high isolation, high linearity, <50 ns switching	Radar, EW, secure communications
QPC2420	Reflective SPDT Switch	Up to 30 GHz	Wideband coverage, high linearity, fast switching, compact footprint	Wideband radar, SATCOM, test & measurement
QPC2180	Reflective SP8T Switch	Up to 8 GHz	High linearity for filter banks, compact integration	Filter banks, multi-band radios
QPC5330	6-bit Digital Step Attenuator	Up to 15 GHz	Precise attenuation, glitch-safe operation, SPI/I2C control	Signal conditioning, radar/EW
QPC5430	6-bit Digital Step Attenuator	Up to 30 GHz	Wideband attenuation, high linearity, daisy-chain support	Test & measurement, microwave backhaul, communications systems

Unlike conventional approaches that rely on multiple narrowband components or mixed-vendor solutions, Qorvo’s SOI portfolio enables designers to standardize switch and attenuator functions into a scalable RF control platform. This reduces routing complexity, minimizes calibration effort, and accelerates design reuse across programs. In comparison to legacy GaAs switches, Qorvo delivers simpler biasing and easier integration while maintaining RF performance for modern defense and aerospace systems. With discrete multi-part RF control chains, designers can reduce BOM complexity, board space, and integration burden while improving signal integrity and simplifying future upgrades.

The portfolio aligns with key industry trends, including wider bandwidth radar and EW systems, more agile signal routing requirements, and increasing pressure to reduce SWaP while accelerating time to market. By combining optimal control integration, fast switching, high isolation, strong linearity, and flexible digital control, Qorvo enables designers to modernize RF control architectures without increasing system complexity.

The post Qorvo Eliminates Negative Bias in New RF Control Portfolio appeared first on ELE Times.

Recent frontier LLM inference benchmarks have highlighted a recurring pattern. GPU-based systems deliver outstanding throughput when latency is not a concern, but their performance drops sharply once real-time response requirements are imposed.

This behavior is sometimes attributed to software inefficiencies or suboptimal system tuning. In reality, the root cause lies much deeper. It reflects a fundamental mismatch between how GPUs are architected and how autoregressive inference works.

LLM inference: Prefill versus generation

To understand this limitation, it is useful to examine the two distinct phases of LLM inference: prefill and generation.

During the prefill phase, the model processes the entire input prompt in one pass. The prompt is tokenized, embedded, and propagated through every layer of the transformer network. At each layer, the model computes the attention relationships among all tokens and builds the key-value (KV) cache, which stores the intermediate data needed for subsequent token generation.

This stage maps extremely well onto GPU hardware. GPUs were designed to execute thousands of identical operations in parallel. In the prefill phase, the model performs massive matrix multiplications over large tensors, exactly the type of workload for which GPUs excel. When all tokens are available upfront, the calculations can be distributed across tens of thousands of cores, resulting in very high arithmetic utilization.

The generation phase is fundamentally different.

Once the KV cache has been created, the model begins producing output tokens one at a time. Each token depends on all tokens that came before it. This sequential dependency means that, regardless of how much hardware is available, the model cannot generate the next token until the current one has been completed.

For every generated token, the model must read the parameters for every layer, consult the KV cache, compute the next token probabilities, and then repeat the autoregressive process. The amount of computation per token is relatively modest, but the amount of data movement remains substantial.

Two faces of GPU architecture: Why modern GPUs struggle with real-time latency constraints

This is where the GPU architecture begins to work against the workload.

GPUs achieve peak efficiency when they execute large, highly parallel workloads with regular memory access patterns. Token generation offers neither. The workload is small, inherently sequential, and dominated by repeated memory accesses rather than dense arithmetic. Many of the GPU’s compute units remain idle while the device waits for data to arrive from high-bandwidth memory.

In other words, generation is not compute-bound; it’s memory-bound.

The distinction is crucial. In a compute-bound workload, adding more arithmetic units improves performance. In a memory-bound workload, performance is limited by how quickly data can be moved to the processors. Once memory bandwidth becomes the bottleneck, additional compute resources provide diminishing returns.

This explains why GPUs can appear extraordinarily efficient when throughput is measured without latency constraints. In that scenario, inference servers are free to buffer requests and combine them into large batches. Batching allows the system to process many token streams simultaneously, effectively transforming numerous small sequential tasks into a larger parallel workload that better matches the GPU’s strengths.

The role of batch sizes in GPU’s utilization

At first glance, batching in AI inference may appear straightforward. Unlike image inference where every sample in a batch completes simultaneously, LLM inference involves many conversations progressing independently and asynchronously. Some requests finish quickly, others may continue for hundreds or even thousands of decoding iterations, and new requests may arrive continuously while older conversations are still active.

The workload therefore becomes highly dynamic and irregular. Specifically, the generation of each request ends only when the model produces a special “end-of-sequence” token indicating that the response is complete.

This characteristic fundamentally changes the nature of inference scheduling.

This is where continuous batching becomes essential. Continuous batching is the runtime orchestration algorithm responsible for managing the simultaneous execution of multiple conversations across the same accelerator resources. Instead of treating inference as a sequence of isolated batches, the scheduler continuously inserts, removes, pauses, and resumes requests as tokens are generated.

The objective is to maximize hardware utilization while minimizing user-visible latency. As batch sizes increase, hardware utilization rises and throughput improves dramatically. However, batching comes at the cost of response time.

When users expect low latency, the system cannot afford to delay requests while waiting to accumulate a large batch. Each request must be processed almost immediately. As batch sizes shrink, the GPU loses the parallelism needed to keep its compute resources busy. Utilization falls, and throughput drops accordingly.

This is the central architectural limitation of GPUs in LLM inference.

The issue becomes even more pronounced when the same accelerator must handle both prefill and generation. Prefill is a large, compute-intensive task, while generation consists of many smaller, latency-sensitive operations. When new prompts arrive, the system may need to interrupt ongoing token generation to perform prompt processing. These context switches, often referred to as preemption, increase latency and reduce efficiency further.

Inference disaggregation: A clever shortcut to mitigate GPU’s inefficiencies

To mitigate this problem, system designers have begun disaggregating inference. Instead of assigning both phases to the same accelerator pool, they dedicate one group of GPUs to prefill and another to generation. The prefill GPUs build the KV cache and transfer it to the generation GPUs, which decode tokens independently.

This separation eliminates interference between the two phases and allows each group of GPUs to operate more efficiently. Prompt processing can proceed continuously without disrupting active token generation, and generation can continue without interruption.

In controlled benchmark environments, where prompt lengths, output lengths, and request patterns are known in advance, this approach can deliver substantial improvements.

Yet the underlying limitation of GPU architectures remains.

Inference disaggregation: Does it scale in real-world applications?

The generation phase is still sequential and memory bound. No amount of software optimization can eliminate the need to read model weights and cached data for each token. The disaggregated approach simply reduces scheduling inefficiencies and isolates the phases so that GPU resources are used more effectively.

Whether this strategy can scale efficiently in real-world applications depends on workload predictability.

The real-world AI services process a highly variable mix of requests. Some consist of long prompts and short responses. Others involve short prompts and long outputs. Demand can shift rapidly over time, changing the ideal ratio between prefill and generation resources.

Adapting to these changes requires dynamically reallocating accelerators. That process is not instantaneous. Devices must be initialized, model parameters loaded, and serving infrastructure synchronized. If traffic patterns are highly volatile, the overhead of reconfiguration can offset much of the benefit.

The broader lesson is that GPU performance in LLM inference is governed by more than raw TeraFLOPS.

The prefill phase showcases the strengths of GPUs, leveraging dense matrix operations and massive parallelism. The generation phase exposes their weaknesses, forcing highly parallel processors to execute a fundamentally sequential, memory-dominated workload.

As a result, the impressive throughput numbers often reported in unconstrained benchmarks can be misleading. They reflect idealized conditions in which batching hides architectural inefficiencies. Once latency constraints are introduced, those inefficiencies become visible.

The challenge for the industry is not simply to build larger GPUs, but to develop architectures and system designs better aligned with the realities of autoregressive inference.

Until then, the most significant limitation in real-time LLM serving will remain the same: generation is a sequential, memory-bound process running on hardware originally optimized for massively parallel computation.

Lauro Rizzatti is a business development executive with VSORA, a technology company offering semiconductor solutions that redefine design performance. He is a noted chip design verification consultant and industry expert on hardware emulation.

Editor’s Note

In a two-part series, contributor Lauro Rizzattti examines how LLM inference forced changes to MLPerf benchmarking. He will illustrate the evolution of the MLPerf benchmark and detail how generative AI forced a radical shift in AI hardware evaluation in the upcoming Part 2.

Related Content

• Strategies to Dominate the AI Accelerator Market
• A closer look at LLM’s hyper growth and AI parameter explosion
• The role of AI processor architecture in power consumption efficiency
• AI GPU computing delivers data-center performance on the factory floor
• The truth about AI inference costs: Why cost-per-token isn’t what it seems

The post The hidden bottleneck in LLM inference and the impact on MLPerf benchmarking appeared first on EDN.

This is a portable lab device help to do experiment like diodes bjt amplifier gain and act as a function generator submitted by /u/dealer-products [link] [comments]

Abundant use of the AI acronym is increasingly evident at various industry events. Strip away the hype layer and look deeper, however, and interesting trends still emerge into view.

This is my third straight year covering Microsoft’s developer-focused conference, following up on the 2024 and 2025 show editions. And interestingly (at least to me), the event timing, both in an absolute sense and relative to other notable industry trade shows, has shifted each year.

• 2024’s Build took place on May 21-23, the week after Google’s I/O developer event (May 14-16) and several weeks before Computex (June 4-7)
• Last year, all three conferences took place on the same week
• And this year, the Google I/O and Microsoft Build cadence returned to separate-weeks spacing, two weeks apart this time. Conversely, Build and Computex were still in the same-week slot.

Why the upfront focus on this seeming nuance? Well, for one thing, Computex conversely is a consumer-tailored show. That’s why, for example, Microsoft and NVIDIA co-announced one new computer (information on which I’ll share shortly) at Computex, while introducing another with a different form factor but the exact same processing subsystem at Build. Plus, in emphasizing a point that is likely already obvious to at least some of you, any chronological spacing between two companies’ events enables the latter to fine-tune its announcements and their messaging to react to the former…and the more spacing the better from a reaction-robustness standpoint.

Speaking of announcements, let’s get to them, shall we? Microsoft CEO Satya Nadella and his various lieutenants, along with a couple of special guests, covered a lot of ground in the 2.5-hour kickoff keynote, the video of which I’ve embedded below. I’ll hit what I thought were the highlights in the following paragraphs.

AI inference-accelerating hardware

About those computers I just mentioned…stop me if you’ve heard this before. Microsoft and a partner roll out new Windows-on-Arm computer platforms, both mobile and mini-desktop in shape, and intended for both consumers and developers. Two years ago, that partner was Qualcomm, the SoCs were the Snapdragon X Elite and Plus, and the consumer mobile systems were the Surface Laptop and Pro (also accompanied by ones from other OEMs, in a nod to Microsoft’s broader Windows-on-Arm aspirations). The developer mini-desktop was the Snapdragon Dev Kit for Windows, which never made it to production: Qualcomm “indefinitely paused” it only a few months later:

This outcome was more than a bit of a surprise to me, albeit not a complete surprise, as I’d been hearing for some time of both chronic hardware and software issues with the platform. That said, I already owned (and still use) its two Qualcomm application processor-based, developer-tailored predecessors, the Qualcomm-branded ECS LIVA Mini Box QC710:

and Microsoft’s “Project Volterra” (officially: Windows Dev Kit 2023) system:

so the Snapdragon Dev Kit for Windows was unsurprisingly on my wish list, too.

Hopefully NVIDIA will have better luck, although the situation still feels somewhat embryonic. Consumer mobile system(s) first: launched at Computex and coming “this fall” at an as-yet-unannounced price is the Microsoft Surface Laptop Ultra, based on NVIDIA’s RTX Spark SoC:

 

While you might not immediately recognize the processor from its new marketing moniker, you’ve heard about it (from me, to be precise) before. It was previously known as the N1 and N1X, as well as the GB10, and it’s the outcome of a co-development project with MediaTek, who contributed the up-to-20-core CPU constellation and reportedly also took lead on full-chip integration, including the NVLink interconnect to the up-to-6,144 core GPU cluster.

The SoC’s development has been lengthy and troubled, if longstanding and widespread rumors are to be believed, and industry analyst skepticism remains existent. It first appeared in a Linux-based system, the DGX Spark (rebranded from its initial name, Project DIGITS), last October:

And now, NVIDIA has determined that the RTX Spark is finally ready for Windows-based laptops (and not just from Microsoft itself, just as was the case two years before with Qualcomm). But not now. “This fall”. At a price to be announced later, but likely stratospheric if due only to the industry constraints-driven currently pricey “up to 128GB of unified memory”. And what about the developer mini-desktop system, the Surface RTX Spark Dev Box, unveiled at Build?

There’s…umm…a waitlist. Microsoft CEO Satya Nadella invited the Build attendees to join him on it. None of which inspires much in the way of confidence. Maybe one or both systems will be available for sale in time to end up on this November’s edition of my yearly “Holiday shopping guide for engineers”, but at this point, I’d be (pleasantly, mind you) surprised.

If you’re once again feeling déjà vu, by the way, it’s because Microsoft and NVIDIA have been here before. The initial attempt at bringing a Windows-on-Arm system to market, the Surface with Windows RT, was based on an NVIDIA Tegra SoC. I personally owned one and ended up tearing it apart after it eventually died. The hardware was first-rate for the time, although a dearth of native software in conjunction with woeful x86 code emulation support doomed it.

That was 2012. Jump forward again to the other, earlier-mentioned déjà vu moment, Qualcomm’s announced partnership with Microsoft in 2024, and I feel compelled to point out that by no means is it seemingly deceased (or even on life support, for that matter). I recently acquired a gently used Microsoft Surface Pro 11 based on Qualcomm’s Snapdragon X Plus to replace my long-in-the tooth Surface Pro X. The SP11 has 16 GBytes of RAM and a 1 TByte SSD and runs solely on its integrated battery all day with ease, even when emulating x86. Microsoft systems based on second-generation Snapdragon X2 Elite (and presumably also Plus) SoCs are seemingly coming soon. And on a similar note, Microsoft’s still churning out branded systems based on x86 CPUs, too, with most recent updates less than a month ago.

Agentic-centric O/Ss

One particularly memorable quote from Satya Nadella in the keynote was the following:

“There’s a real platform shift. We’re moving from building operating systems, devices for apps, to agents.”

Indicative of this forecasted shift is Project Solara, explained by means of a conversation between Nadella and Qualcomm President and CEO Cristiano Amon:

along with an Android-derived proof-of-concept demonstration showing agent-based interactions with (and between) a smart speaker with a screen, mobile devices, and intelligent ID cards. Google also spoke a great deal about agentic AI at its I/O developer conference two weeks ago; instead of repeating myself again, I’ll refer you to my coverage of that event for the background info if you need it.

Speaking of agents, Microsoft also announced Execution Containers, which keep agents from accessing unintended, critical regions of other agents and applications, the underlying operating system and system hardware. And for when you want to communicate with them, OpenClaw founder Peter Steinberger showed up on stage by means of introducing Scout, an OpenClaw AI Assistant gateway. If you’re thinking it sounds at least something like Gemini Spark, which Google announced two weeks back, you’re not off-base. Remember my comments at the beginning of this piece about competing-event timing and ordering and effects on later-event messaging?

Homegrown models

Last but not least, let’s touch on an event topic that prompted the “AI Independence” title of this piece. In late April, OpenAI and Microsoft “redefined” their business relationship, in the process fundamentally freeing both companies from the various exclusivity arrangements that had previously defined (and arguably dominated) it. While a “divorce” would be overstating the result, a “softer” term such as “conscious uncoupling” wouldn’t be far off.

One tangible outcome of this redefinition was clearly evident this week, as Mustafa Suleyman, head of Microsoft AI, unveiled seven new homegrown AI models with capabilities spanning image, voice and transcription functions and claimed performance matching if not exceeding that of Google, OpenAI and other competitors’ models, both open- and closed-source. I was particularly interested in Suleyman’s declaration regarding MAI-Thinking-1, the flagship reasoning model, that:

“We trained it from the ground up on clean data, without distillation from third-party models.”

And with that, I’ll wrap up for today. As always, I welcome your thoughts in the comments on the topics I’ve covered here, as well as any others that might have caught your eye—Microsoft’s ongoing research work on quantum computing, for example, including the development of Majorana 2, the sequel to last year’s premier quantum computing chip from the company.

Next Monday, Tim Cook and his CEO successor John Ternus (I’m assuming) will hit the stage to kick off Apple’s yearly Worldwide Developers Conference (WWDC), completing the yearly big-tech-company developer conference triumvirate. I’ll see you back here then, if not before!

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

Related Content

• Google I/O 2026: Agentic AI gets serious
• Microsoft Build 2025: Arm (and AI, of course) thrive
• Microsoft’s Build 2024: Silicon and associated systems come to the fore
• A holiday shopping guide for engineers: 2025 edition

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Altera has partnered with Mercury Systems and VadaTech to expand its Agilex 9 FPGA ecosystem with COTS VPX boards for mission-critical defense platforms. These solutions integrate Agilex 9 medium-band Direct RF FPGAs into VPX architectures, including SOSA-aligned OpenVPX, to help defense customers accelerate time-to-market, reduce SWaP, and enable flexible software-defined RF capabilities.

The Agilex 9 FPGAs combine RF data converters, FPGA fabric, and high-speed transceivers into a unified, programmable architecture, enabling real-time processing of large volumes of RF data at the edge. This integration supports distributed, multi-domain operations that require rapid decision-making and adaptation to changing mission requirements. The devices deliver the bandwidth, performance, and I/O needed for demanding embedded applications such as adaptive radar, cognitive electronic warfare, and secure, software-defined communications.

Mercury Systems’ DRF5660 boards and VadaTech’s VPX540 boards with Agilex 9 Direct RF AGRM027 FPGAs are available for order today.

Agilex 9 Direct RF series

Altera

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Digital signal controllers (DSCs) in Microchip’s dsPIC33CK Value Line provide real-time control for cost-sensitive designs. Starting at $0.51 each, they offer consistent pricing regardless of order size. The 16-bit controllers deliver 100-MHz deterministic processing, high-resolution PWM, and a 12-bit ADC supporting motor control, precision sensing and control, and touch/HMI applications.

A balanced set of peripherals helps reduce external component count, PCB footprint, and overall BOM cost. With flash memory ranging from 32 KB to 256 KB and compatibility across the dsPIC33CK family, the Value Line DSCs enable scalability and migration to future designs. The devices integrate a 12-bit ADC capable of up to 2 Msamples/s, four PWM pairs with resolution down to 2 ns, and on-chip analog comparators with a 12-bit DAC. Communication interfaces include CAN FD, LIN, SENT, UART, SPI, and I2C.

To accelerate evaluation and development, Microchip offers the dsPIC33CK Value Line Curiosity Nano evaluation kit with an onboard debugger. The evaluation platform supports the Curiosity Nano base for Click Boards and a touch adapter board for touch applications. A motor control DIM is also available for rapid prototyping of motor control designs.

Value Line DSCs are available directly from Microchip, its sales representatives, or authorized distributors.

dsPIC33CK Value Line

Microchip Technology 

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Keysight’s RF Circuit Simulation Professional software now enables engineers to document their design workflow on an executable whiteboard. The software replicates design decisions while capturing simulations, optimizations, decision trees, and parameters derived from prior analyses. Each step generates editable Python code that can be saved, shared, replayed for design reviews, and redeployed across the Keysight Advanced Design System (ADS), Cadence Virtuoso, and Synopsys Custom Compiler environments with full design data traceability.

Design teams often face workflow inefficiencies, simulation bottlenecks, and knowledge-transfer challenges. Engineers can build workflows visually on an executable whiteboard while the software automatically generates corresponding Python scripts. The platform executes simulations, optimizations, and design decisions in sequence, with support for decision-based loops and parameter settings.

Each workflow becomes a repeatable methodology that can be shared across teams, reused, and driven by AI. Captured workflows help preserve RF design expertise while creating structured design data that can support future AI-driven automation and training. Design review and tapeout tasks that previously required manual configuration now execute automatically.

RF Circuit Simulation Professional

Keysight Technologies 

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Diodes’ APK43070Q synchronous buck controller integrates a USB Type-C PD 3.1 source controller, simplifying automotive single- and multi-port charging designs. Operating from a 4-V to 36-V input, it enables USB Type-C charging up to 140 W. The device supports USB extended power range (EPR) and adjustable voltage supply (AVS) up to 28 V, along with standard power range (SPR) and programmable power supply (PPS) up to 21 V.

The constant-frequency controller features integrated drivers, optimized dead time, and elevated gate drive voltage for efficient mid- to high-power charging using external N-channel MOSFETs. This allows flexible MOSFET selection to balance thermal performance and power loss. A VIN DC pass-through mode further improves converter performance by enabling the high-side MOSFET to act as the VBUS switch, eliminating the need for an additional output switch.

An I2C interface with a controller/target addressing scheme enables power sharing across up to eight USB Type-C ports via resistor selection without an external MCU. The APK43070Q also includes overvoltage, overcurrent, undervoltage, and thermal protection. 

The APK43070Q is priced at $0.80 each in 1000-unit quantities.

APK43070Q product page

Diodes Inc.

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Pico Technology has launched the PicoScope 5000E series of USB-C oscilloscopes for analog, digital, and mixed-signal debugging. The four-channel scopes provide true 16-bit resolution with bandwidths to 200 MHz, sample rates to 2.5 Gsamples/s, and up to 1 GS of memory. PicoScope 5000E Plus models also offer a switchable 8-bit high-speed mode that raises bandwidth to 500 MHz, sample rates to 5 Gsamples/s, and memory to 2 GS.

With an ultra-low-noise front end, the oscilloscopes achieve a noise floor below 22 µV RMS and total harmonic distortion better than -73 dB. The resulting dynamic range helps reveal small-amplitude components, ripple, distortion, and other anomalies that lower resolution or noisier instruments can miss.

The compact, portable scopes connect to a host computer through a SuperSpeed USB 3.0 Type-C interface. For debug and validation, Pico 7 software provides more than 40 serial protocol decoders, advanced math channels, automated measurements including power analysis, multi-capture analysis, and measurement and mask limit testing. The Pico SDK supports custom application development using C, C#, C++, Python, MATLAB, and LabVIEW.

The PicoScope 5000E series is available in four-channel and 4+16-channel mixed-signal oscilloscope variants, with bandwidth options from 60 MHz to 500 MHz depending on model and operating mode. Units are sold through authorized distributors worldwide and directly from Pico Technology.

PicoScope 5000E product page

Pico Technology 

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🏆 Міжнародний конкурс студентських наукових робіт зі штучного інтелекту 2026 kpi ср, 06/03/2026 - 23:33