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Bosch is expanding its portfolio for diesel components with the new DCP 800 test bench. The abbreviation DCP stands for Diagnostic Common-Rail Pump and refers to a test bench specially developed for common-rail high-pressure pumps. The system helps workshops meet the increasing demands for efficiency, precision, and economy in diesel service.

High-performance technology for precise test results

The DCP 800 enables the testing of common-rail pumps for passenger cars, commercial vehicles, and off-road applications at pressures of up to 2,700 bar. A powerful 25-kW electric motor is directly connected to the drive mechanism and operates without intermediate systems. This drive concept supports precise and reliable testing, particularly for commercial and heavy-duty vehicle applications. Automated inlet and counter-pressure regulation, as well as electronic flow measurement, further contribute to measurement accuracy.

Efficient workflows in the workshop

A key advantage for workshops is the high efficiency of the test sequences. With test times of around 20 minutes – regardless of the application – service processes can be significantly accelerated. At the same time, a quick setup process combined with predefined, standardized test plans ensures simple and repeatable test execution. Optimized filtration, cooling, and measurement systems support automated maintenance intervals and help reduce operating costs.

The test bench covers a broad spectrum of Bosch common-rail pumps, including the CP1 to CPN6 series. Furthermore, it is compatible with existing generations of Bosch test kits such as CP1, CP3, and CBX8 from EPS 815/708 systems. This allows workshops a gradual and economical transition to the new testing technology.

Flexible integration into different workshop environments

A newly developed drive coupling system, which enables the quick mounting of different pump types, provides additional flexibility in the workshop. An adapter solution ensures that older coupling designs can continue to be used. The ergonomic machine concept with three-sided access supports comfortable and safe operation.

The operating computer with a screen for controlling and monitoring the test sequences can be positioned on either side of the test bench, depending on workshop requirements. This allows the DCP 800 to be easily adapted to different workshop specifications. Workshops benefit from optimized workflows, ergonomic operation, and seamless integration into their existing workspace.

The DCP 800 complements the existing Bosch test benches for common-rail injectors and enables workshops to perform comprehensive testing of pumps and injectors from a single source. The new test bench will make its debut at Automechanika Frankfurt from September 8 to 12, where it will be showcased live for the first time.

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For the past decade, the artificial intelligence landscape followed a remarkably simple recipe: if you wanted better AI, you built larger models, fed them more data, and added more GPUs. The formula worked, and it worked spectacularly.

Every new generation of hardware enabled larger neural networks, and larger neural networks broadened their reach. What began as image recognition evolved into agents capable of writing essays, generating software code, and creating realistic images and videos. The world grew accustomed to a simple equation: more GPUs meant more intelligence.

However, in the past couple of years, something changed. As we enter the fourth generation of AI—enterprise AI—the industry is discovering that the old recipe is no longer sufficient. The problem is not that GPUs have become obsolete. Far from it: GPUs will remain indispensable for training the giant, diverse models that underpin modern AI.

The problem is that enterprise AI asks these machines to solve a fundamentally different problem, and the assumptions that drove the generative AI revolution are starting to break down.

Enterprise AI operates by a different set of rules

Most people still think of AI as a chatbot: ask a question, receive an answer, end of interaction. That model defined the generative AI era. Enterprise AI, however, plays by different rules.

Imagine an AI assistant embedded inside a company. It starts the day by reading emails, analyzing spreadsheets, interpreting charts, reviewing contracts, listening to conference calls, and consulting internal databases. It collaborates with other AI agents, prepares reports for employees, and continuously updates its understanding as new information arrives. This is no longer a sequence of isolated interactions; it is continuous reasoning.

AI never truly stops working. It shifts constantly between tasks, maintains context over long stretches of time, and makes thousands of small decisions throughout the day. That may sound like a subtle difference.

But from a computing perspective, it changes everything.

Why GPU architectures struggle with enterprise AI inference

For years, AI systems were optimized for one metric: floating-point operational throughput, or FLOPS. The goal was straightforward: maximize the computation performed every second. GPUs excel at this because they were originally designed to process thousands of identical operations simultaneously.

Rendering video game graphics demanded enormous parallelism, and that same parallelism proved extraordinarily effective for training neural networks. Training, after all, is a highly parallel problem: huge volumes of data can be processed at once, and GPUs thrive under these conditions.

Inference is a different story. Inference is the stage where AI produces answers for users in real time. Unlike training, every new token generated by a large language model depends on the token that came before it. There is no shortcut around this sequential dependency. No matter how many GPUs are thrown at the problem, the system still generates one token at a time.

This is where a new bottleneck emerges. For decades, engineers focused on increasing computational performance while memory performance advanced at a far slower pace. Processors became extraordinarily fast; memory did not keep up. This imbalance is known as the memory wall.

Today, the memory wall stands as one of AI’s most stubborn obstacles. Large language models contain hundreds of billions of parameters, and some already contain trillions. Those parameters cannot fit inside a processor. Every time the model generates a response, it must continuously retrieve information from memory. Increasingly, AI systems spend more time moving data than performing calculations. In fact, in many deployments, far more energy is consumed transporting information than computing the answer.

This reality exposes a fundamental weakness of the GPU-centric approach. GPUs were built to maximize parallel computation; they were never designed to minimize data movement. During the generative AI era, the limitation could be partially hidden by batching many user requests together. Large batch sizes kept GPUs busy and delivered impressive throughput numbers.

Enterprise AI changes the economics

An AI assistant participating in a meeting cannot wait for hundreds of requests to accumulate before responding. A medical system helping a physician interpret an image cannot afford noticeable delays. An AI agent coordinating supply chains or financial operations cannot pause while batches form. Enterprise AI demands immediate responses. Latency becomes far more important than throughput.

Here lies the trap for GPU-based inference: to meet the latency constraints of the application, the GPU is forced to operate with small batches, and small batches impact the very utilization that made the GPU economical in the first place. The architecture is not broken; it’s simply being asked to run against its own design assumptions, and its performance degrades significantly as a result.

This shift is forcing the industry to ask a different question. Instead of asking how many operations a processor can execute per second, engineers are increasingly asking how quickly information can move through the system. It may sound like a small distinction. It is, in fact, a critical architectural change.

For years, AI hardware was designed around compute: the processor sat at the center of the system, and memory existed primarily to feed it. That philosophy is beginning to reverse. Fast memory access is becoming the center of architecture.

The goal is no longer to build faster arithmetic units. It is to increase data bandwidth and shrink the distance information must travel. Every millimeter matters because every movement consumes energy, introduces latency, and limits scalability.

The memory-centric revolution: From adding FLOPS to moving bytes faster

This realization is driving a remarkable wave of innovation across the semiconductor industry. Some companies are building processors with enormous amounts of on-chip memory to keep data local. Others are developing specialized inference engines optimized for autoregressive token generation.

New architectures activate only the portions of a model required for a given task, avoiding unnecessary computation altogether. At the same time, researchers are exploring near-memory and in-memory computing, two approaches that bring computation physically closer to where the data resides.

Among the many pioneers proposing new architectures, a few stand out. Cerebras has demonstrated that placing an entire processor on a single silicon wafer can dramatically reduce latency and communication bottlenecks. Groq has focused on highly deterministic, low-latency inference rather than peak throughput.

d-Matrix attacks the memory problem directly by bringing memory and computation closer together. SambaNova is exploring reconfigurable dataflow architectures, while Etched has aggressively specialized its hardware for transformer models.

Beyond GPUs: Rewriting the rules of AI inference

Another example is a next-generation inference architecture. It rests on three interconnected and mutually reinforcing innovations: a memory-centric design philosophy; a high-bandwidth, low-latency, deterministic pipelined data flow; and a hybrid compute fabric that fuses large arrays of tensor cores with on-the-fly reprogrammable DSP cores. Together, they address the fundamental bottlenecks that have long constrained the performance, efficiency, and scalability of AI inference hardware.

The first innovation, the memory-centric approach, marks a fundamental departure from conventional inference architectures. Traditional systems lean heavily on multi-level KV (key-value) caches shuttled through external memory, introducing latency and energy overhead each time the compute engines retrieve or update cached values.

The KV cache is not eliminated. Instead, its economics is transformed. Massive arrays of local registers, positioned in extreme physical proximity to their corresponding compute engines, allow the architecture to hold far larger contexts and model parameter sets close to the compute fabric, making every KV cache access dramatically more efficient.

This reduction in data travel distance cuts memory access latency, relieves pressure on the memory bus, and yields gains in power efficiency. The result is an architecture that sustains higher throughput while consuming less energy, an advantage in large-scale deployments where energy costs and thermal management dominate operational concerns.

The second innovation, the high-bandwidth, low-latency pipelined data flow, addresses the inefficiencies inherent in the single instruction, multiple threads (SIMT) execution model that dominates GPU-based inference today. SIMT architectures impose a heavy memory tax: threads must frequently synchronize and stall while waiting for memory operations to complete, burning both time and power.

By replacing SIMT with a deeply pipelined data flow, the chip ensures that data moves continuously and predictably through the compute stages, eliminating synchronization stalls and sharply reducing the latency and power penalties of traditional GPU memory access patterns. The pipelined approach also delivers deterministic, real-time inference performance, an increasingly important requirement as AI systems move into latency-sensitive applications such as enterprise AI, autonomous driving, robotics, and real-time speech.

The third innovation, the hybrid tensor core and DSP compute fabric, adds a new dimension of computational power and flexibility. Tensor cores are highly optimized for the dense matrix and vector operations that dominate transformer inference, but many workloads also require specialized signal processing, custom activation functions, and non-standard numerical transformations that tensor cores handle poorly.

By augmenting the tensor core arrays with reconfigurable DSP cores, reprogrammable on the fly and driven by a rich instruction set in which every instruction executes in a single clock cycle, the hardware can adapt dynamically to the computational demands of each model and each inference task.

This versatility carries weight for agentic AI. Pre- and post-processing tasks that would normally fall to the host CPU can be executed locally on the accelerator, minimizing CPU-accelerator traffic and freeing host compute capacity for other work. It also future-proofs the architecture against a model landscape that refuses to stand still.

Taken together, these three innovations from VSORA offer a purpose-built solution for modern, scalable AI inference in data centers and at the edge. The result is greater efficiency, lower latency, and broader adaptability than conventional GPU-centric approaches that translates into a lower cost structure and higher ROI for cloud service providers.

On the verge of next architectural transition

Computing history teaches that architectural transitions occur whenever a dominant technology encounters the limits of its original design assumptions. For decades, CPUs were the universal solution until graphics workloads grew so demanding that GPUs emerged as a new specialized architecture. Today, GPUs face a similar moment. They are not being replaced; they are being complemented.

The winners of the next AI race will not necessarily be the companies that build the largest clusters of GPUs. They will be the companies that build systems capable of the smartest data access and management, consuming less energy, responding faster, and reasoning more efficiently.

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

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The post Enterprise AI: Reshaping AI processor architectures appeared first on EDN.

Singapore-based fabless silicon photonics company SILITH Technology has announced the first mass-production wafer delivery of photonic ICs from the Singapore fab of foundry United Microelectronics Corp (UMC) of Hsinchu, Taiwan, advancing the partnership between the two companies to scale next-generation silicon photonics manufacturing...

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Reach into your pocket and find your keys without looking. You just did something no robot can reliably do. You couldn’t see into your pocket, yet your fingers sorted keys from coins and lint, registered the weight and the cut edges, and closed around the right object. That was touch, quietly doing the kind of work that sight simply cannot.

Artificial intelligence (AI) has learned to see. It has learned to listen. Increasingly, it can reason, plan, converse, and generate sophisticated solutions to complex problems. Yet despite astonishing advances in foundational models and robotic computer vision, the next generation of intelligent machines remains constrained by a surprisingly simple limitation: ask a robot to pick up a paper cup, a grape, or a crumpled receipt, and the illusion breaks. It hesitates. It crushes the cup or drops the grape. The brain got smart far faster than the hands did.

This limitation isn’t because robots lack intelligence. It’s because they lack touch. For decades, AI has largely been about perception—understanding the world through images, language, and sound. Physical AI systems—that must operate and interact directly in the real world—reshape the challenge entirely. Instead of simply interpreting the world, intelligent machines must interact with it. They must grasp, manipulate, assemble, lift, carry, and collaborate efficiently and safely alongside people.

The gating modality for physical AI, therefore, is no longer being able to see. It’s being able to feel. Vision gave robots a view of the world. Touch, or multimodal sensing, is what will finally let them reach into it.

Figure 1 Physical AI doesn’t stop at perception. Multimodal sensing integrates vision, touch, and edge AI to enable robots to understand, adapt to, and safely interact with the physical world in real time. Source: Synaptics

The shift from perception to interaction

The last decade has been dominated by extraordinary progress in perception technologies. Computer vision systems now recognize objects with remarkable accuracy. Large language models (LLMs) can break down complicated tasks into logical sequences of actions. Robots increasingly understand what they are looking at.

Seeing and understanding, however, is not the same as taking action. A warehouse robot may correctly identify hundreds of products on a shelf yet struggle to remove a flexible package without crushing it. A humanoid robot may recognize a wine glass instantly but fail to grasp it securely and with the carefulness required of a fragile object.

To understand the shift from perception to interaction, it helps to contrast two flavors of autonomy by comparing autonomous vehicles with robotic manipulation. A self-driving car spends nearly all its effort avoiding contact. For an autonomous vehicle, touching something usually represents failure.

A robotic hand exists for precisely the opposite purpose. Its job is to make contact deliberately, continuously, and intelligently. Every successful grasp depends not simply on locating an object, but on understanding how that object responds when the instant contact is made. That information cannot be seen. It must be felt.

Vision plans, touch executes

Researchers increasingly describe robotic manipulation with a simple phrase: vision is for planning, and touch is for execution. Vision excels at global understanding. Cameras determine where objects are located, estimate pose, classify materials, and plan trajectories. They provide the strategic overview necessary for intelligent action.

But the moment robotic digits close around an object, vision begins to fail. The contact point disappears behind the hand itself. Cameras cannot observe friction. They cannot determine whether a paper cup is slightly damp, whether a cardboard box is heavier than expected, or whether a glass has begun slipping between two fingertips.

These are not visual problems. They are tactile problems. Humans solve them effortlessly. We tighten our grip on a slippery glass before it falls. We pick up an egg and instinctively apply exactly enough force—not too little, not too much. We rarely think about these abilities because our nervous system performs them automatically. For robots, they remain among the hardest problems in engineering.

Figure 2 Touch happens fast and intelligence happens faster. In just 70 milliseconds, multimodal touch sensing and edge AI transform raw tactile signals into intelligent grip control for safer, more dexterous robots. Source: Synaptics

The next robotics frontier: The science of the “slip”

Picture a condensation-slicked glass of water in your hand, eyes closed. As it begins to slide, you don’t need to see it to react. Your fingertips pick up a faint change in vibration and pressure, and your brain issues a “smart squeeze”—just enough extra force to arrest the slide without shattering the glass. The whole loop, from sensation to correction, runs below the level of conscious thought.

That loop is one of the hardest things to reproduce in a robot. Machines can compute trajectories, yet they remain clumsy about the feel of friction. The traditional answer was pre-programmed rigidity: apply a fixed grip force and hope the object—steel or sponge—cooperates.

Today’s physical AI system developers, however, look for something more subtle: the moment, invisible to the eye, when a stable hold begins to come apart. Contrary to intuition, objects rarely transition instantly from being secure to being dropped. Slip begins gradually.

At microscopic scales, every surface consists of tiny peaks and valleys. As force builds across a contact patch, the outer regions begin slipping while the center continues gripping. Engineers call this incipient slip, and detecting it early transforms robotic control. Instead of reacting after failure, robots can intervene before failure occurs.

This failure intervention requires two complementary sensing systems that mirror the layered design of human skin. A slower channel continuously measures pressure, shear forces, and load distribution across the fingertip. A much faster channel listens to high-frequency vibrations that signal the earliest stages of slipping. Together they create something remarkably similar to the layered sensing architecture found in human skin. One channel feels pressure, the other hears friction.

Only by combining both can a robot understand what’s actually happening at the point of contact.

Figure 3 Physical AI is a continuous feedback loop, not a one-time decision. True physical AI is possible when vision, touch, and edge intelligence work together in a continuous cycle of sensing, thinking, acting, and adapting. Source: Synaptics

Why touch changes everything

A useful multimodal sensing system must answer four fundamental questions.

First, has contact actually occurred? Millimeter-scale positioning errors matter. A robot must know the precise instant it touches an object rather than empty space.

Second, how much force is being applied? Picking up a steel bracket requires entirely different forces than handling fruit, laboratory samples, or medical devices.

Third, is the object beginning to slip? Perhaps the most important capability of all is detecting incipient slip—the microscopic changes that occur before an object falls.

Fourth, how does it feel? While the internet contains unlimited visual data, it contains almost no tactile experience data. No image can tell an AI application what wet glass feels like, how fabric stretches, or how friction changes as an object rotates within a grasp. The only way to learn these interactions is through physical contact. Touch provides ground truth: rich, labeled physical data about what real contact feels like, which is precisely what physical AI systems require.

Intelligence belongs at the edge

Touch also changes where intelligence must live. Unlike language models, tactile decisions cannot wait hundreds of milliseconds for cloud processing or centralized computation. If a glass begins slipping, every millisecond matters. Human grip corrections occur in roughly 70 milliseconds. A robot that must transmit raw sensor data to a central processor before deciding how to respond has already lost valuable time.

Instead, tactile intelligence must move to the edge. Each fingertip becomes an intelligent sensing node, interpreting pressure, force, and vibration locally before sending only meaningful events to higher-level control systems. This architecture mirrors biology: individual nerve endings process local information before communicating with the brain. The brain receives distilled information about events that matter rather than monitoring every sensory receptor continuously.

Physical AI increasingly requires the same distributed architecture. Vision determines the objective. Local intelligence manages contact. Higher-level AI coordinates the task. Touch increases the likelihood that each task is executed successfully.

Physical AI architecture: Reliability is more important than resolution

Much of today’s discussion around robotic touch as the path to dexterous machines focuses on sensor resolution. Resolution matters, but reliability matters more. Building an impressive laboratory demonstration that executes large numbers of touch events is relatively straightforward. Building a tactile sensor that performs accurately after millions of grasps in factories, warehouses, and hospitals is vastly more difficult.

Every tactile technology has weaknesses. Soft materials wear. Temperature changes electrical characteristics. Motor noise contaminates tiny signals. Elastomers gradually develop hysteresis, remembering previous deformations instead of returning instantly to baseline.

Optical sensors produce exceptionally rich data but require bulky camera systems. Magnetic sensing performs beautifully until exposed to external magnetic fields. Piezoelectric materials detect vibration extraordinarily well but cannot measure static force.

The challenge is not finding a perfect sensing modality. The challenge is engineering reliable systems that compensate for imperfections while surviving years of real-world use. Success requires expertise that extends well beyond robotics to materials science, signal processing, embedded intelligence, silicon design and manufacturing, firmware, and systems engineering, all working together as a single discipline. Ultimately, reliability—not raw sensitivity—is what determines whether physical AI scales beyond research laboratories.

Why this moment matters

The urgency surrounding physical AI development is driven by timing. In industrial applications, for example, humanoid robots are moving from prototypes toward mass commercial deployment according to industry research analysts. Conservative estimates from Goldman Sachs Research predict 1.4 million active humanoids will be working in global manufacturing, warehouses, and logistics by 2035. Morgan Stanley projects an even higher trajectory, citing massive production scale and adoption of 13 million service robots working along humans in the same timeframe.

The opportunity extends even further. Prosthetic limbs require tactile feedback to restore natural function. Data collection systems need tactile sensing to train future manipulation models.

Large-area robotic skin improves safe collaboration between humans and machines. Every application depends upon extracting trustworthy physical information from an inherently noisy world, and regardless of form factor, every Physical AI system eventually encounters the same bottleneck: hands or more precisely, contact.

Whether a machine uses five-fingered humanoid hands, industrial grippers, or specialized end effectors, it must regulate force, detect slip, and manipulate objects safely. Touch, therefore, becomes a foundational capability rather than an optional enhancement.

From digital to physical intelligence: Relying on touch for physical AI

History suggests that every major computing revolution has been enabled by a new interface. The graphical user interface transformed personal computing. The touchscreen transformed mobile computing. Touch now appears poised to transform robotics.

Just as computer vision unlocked autonomous perception, tactile intelligence will unlock autonomous manipulation. The companies that succeed in this technology space will not simply build better sensors. They will build complete sensing systems that combine advanced materials, embedded intelligence, signal processing, edge AI, robust manufacturing, and decades of experience extracting reliable information from imperfect physical environments. That combination—not any single breakthrough—will define the next generation of physical AI.

Physical AI is often described as giving machines a brain. In reality, we have spent the past decade building remarkably capable brains. What robots still lack is a nervous system: the ability to feel force, sense friction, recognize instability before failure, and transform contact into understanding.

The future of intelligent machines will not be determined solely by larger models or more powerful processors. It will be determined by whether those machines can interact with the physical world with the same confidence that humans have.

Vision gave AI the ability to understand the world. Touch will give it the ability to change it.

Satish Ganesan joined Synaptics in November 2019 and serves as senior VP and GM of Edge Interface & Sensing Division and Chief Strategy Officer. Prior to Synaptics, Satish served as chief product officer of Keyssa, a wireless startup focused on short-range connectivity. He also held several executive positions at Broadcom and Xilinx.

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The post Missing sense: Why physical AI can see everything and feel nothing appeared first on EDN.

In booth B03 (Hall 16) at Power Electronics, Intelligent Motion, Renewable Energy and Energy Management (PCIM Asia 2026) in Shenzhen, China (26–28 August), Efficient Power Conversion Corp (EPC) of El Segundo, CA, USA — which makes enhancement-mode gallium nitride on silicon (eGaN) power field-effect transistors (FETs) and integrated circuits for power management applications — is showcasing its newest-generation GaN technology, demonstrating how its Gen7 GaN platform enables compact, high-performance power architectures for intelligent motion control and AI power delivery. The latest generation of eGaN devices combines ultra-low on-resistance, low switching losses and high-frequency operation to simplify system integration while improving efficiency, dynamic response, and thermal performance...

Epitaxial deposition and process equipment maker Veeco Instruments Inc of Plainview, NY, USA says that vertically integrated gallium arsenide (GaAs)-based quantum dot (QD) diode laser developer and manufacturer Innolume GmbH of Dortmund, Germany has purchased a GEN2000 molecular beam epitaxy (MBE) system to expand production for next-generation optical transceivers and silicon photonics applications. The investment supports the increasing demand for optical connectivity solutions for hyperscale AI infrastructure and cloud data centers...

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"Воїни та квіти" в Українсько-Японському центрі КПІ Інформація КП вт, 07/21/2026 - 16:00

This easy-to-build PWM source is a handy testing tool for hobbyists, technicians, engineers and other RC-based device developers.

I work at a university where I’m charged with designing and developing all manner of circuits and systems for students, faculty, researchers, and the like. These circuits include a diversity of projects relying on the standard remote control (RC) pulse-width modulation (PWM) signal for their operation.

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

This signal is generally defined as a TTL pulse train having a frequency of 50Hz and a width ranging from 1ms to 2ms. While these parameters can be programmed into most waveform generators, alternatively having a small, dedicated-function gizmo at the ready can prove to be really convenient! Figure 1 shows what I came up with.

Figure 1 The standard RC PWM signal used to control a myriad of devices is usually obtained using transmitters and receivers. This alternative RC servo operator circuit implements a convenient alternative jig that simplifies attaining various testing objectives.

Components C1, U1, and C2 comprise a low dropout 5V regulator. More generally, any 5V regulator IC can be employed, so long as the dropout is under a volt. Typically, RC servos run on 6V, though some will run on as many as 8V. The nominal input, indicated as 6V here, is forwarded to the servo output for convenient experimentation as shown.

The PWM source is based on Analog Devices’ nice-and-simple LTC6992-1. The trick is selecting the right resistor values, which are shown in Figure 1. Rather than generate a PCB, I soldered up the circuit using proto-board, through-hole components, and adapter boards for both ICs. If done on a PCB using surface-mount components, the entire circuit will take up only about 1 in2.

I don’t personally keep an RC transmitter and receiver combo in my lab. Even if I did, I’d need someone to manipulate the controls while I measured the values of interest on the system I’m developing. With this handy alternative circuit, I can run all my tests without human assistance, not to mention without the RC hardware. It has proven useful on numerous occasions.

Mike Potash is the electronics technician for the College of Engineering at Embry-Riddle University in Daytona Beach, Florida.

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The post Simple, compact circuit conveniently controls devices and systems appeared first on EDN.

Electrification and automation technology provider ABB of Zurich, Switzerland is acquiring Advantics of Saint-Genis-Pouilly, France, which provides silicon carbide-based power conversion solutions (integrating hardware, firmware and software to optimize performance across demanding applications including data centers, industrial microgrids, power generation and EV infrastructure)...

Epiwafer and substrate maker IQE plc of Cardiff, Wales, UK says that greater-than-expected trading in first-half 2026, with strong demand across all core segments, should yield revenue of at least £64m...

Siemens EDA has snapped up a San Diego startup to complement its digital design and implementation capabilities with AI-powered exploration for advanced system-on-a-chip (SoC) architectures. Precision Innovations, a privately held EDA company, helps semiconductor engineering teams evaluate architectural and design trade-offs early in the SoC design cycle for faster time to silicon.

At a time when chip designers face rapidly increasing SoC complexity and the need to explore new architectures faster and more efficiently, Precision Innovations’ AI-powered early design exploration technology is expected to enhance Siemens’ EDA portfolio by accelerating time to silicon and improving power, performance and area (PPA).

The acquisition aims to bring AI-driven chip planning to a much broader set of semiconductor teams. Source: Siemens EDA

Precision Innovations, founded in San Diego, California, in 2019, develops EDA software built on the open-source OpenROAD framework to help engineers evaluate design feasibility, reduce design iterations, and accelerate time to market. “Together, we can help designers leverage AI to evaluate thousands of design options earlier in the process and focus on innovation where it matters most,” said Tom Spyrou, CEO of Precision Innovations.

Precision Innovations will become part of Siemens EDA’s Digital Design Creation software team to complement the company’s end-to-end digital flow that spans architecture, implementation, and the full silicon lifecycle management of SoCs. “With Precision Innovations joining Siemens, we will expand our ability to support customers as they face rapidly increasing SoC complexity and the need to explore new architectures faster and more efficiently,” said Ankur Gupta, executive VP of IC portfolio at Siemens EDA.

The acquisition, subject to customary conditions, is expected to complete in the third quarter of 2026.

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The post Siemens EDA acquires SoC toolmaker Precision Innovations appeared first on EDN.

For coverage of all the key business and technology developments in compound semiconductors and advanced silicon materials and devices over the last month...

Навчання, стажування, обміни: КПІ розширює партнерство з Італією kpi пн, 07/20/2026 - 22:37

Linear variable differential transformers (LVDTs) stand out as one of the most reliable displacement sensors in modern engineering. By converting linear movement into proportional electrical signals, they deliver unmatched accuracy and repeatability across demanding environments—from aerospace control systems to industrial automation lines. Their rugged, non-contact design ensures long service life, while their ability to resist electrical noise makes them indispensable wherever precision and stability are paramount.

This article highlights how LVDTs operate, why they define precision sensing, and what are their key applications.

At the heart of an LVDT is a movable ferromagnetic core that shifts within a coil assembly, inducing voltage changes that correspond directly to displacement. This elegant mechanism defines LVDT’s reputation for precision sensing, enabling engineers to capture minute movements with exceptional accuracy and repeatability.

That reliability translates into critical applications: stabilizing aircraft control systems, guiding industrial automation processes, and supporting delicate medical instrumentation. By combining robust design with noise-resistant performance, LVDTs continue to set the benchmark for displacement measurement across diverse fields.

The electromagnetic elegance of LVDTs

Well, let’s dig deeper into the rabbit hole of LVDT design. Beyond the straightforward coil-and-core mechanism lies a finely tuned balance of electromagnetic principles that ensures linearity and stability across a wide measurement range.

Its differential signal output scales cleanly with displacement, canceling common-mode noise and making LVDTs remarkably resilient in harsh environments. This blend of simplicity and sophistication explains why they remain the sensor of choice when precision, durability, and repeatability are non-negotiable.

In practice, an LVDT is a widely used electromechanical transducer that converts the rectilinear motion of a mechanically coupled object into a proportional electrical signal. Its structure features a primary winding centered between two symmetrically spaced, identically wound secondary windings, forming the stationary coil assembly of the sensor.

The moving element is a separate tubular armature—called the core—made of magnetically permeable material. Free to slide axially within the hollow bore of the coil, the core is mechanically linked to the object under measurement. The bore provides ample radial clearance, ensuring no physical contact between the core and coil.

As the core shifts position, the magnetic coupling between the primary and each secondary winding changes, producing displacement-dependent voltage signals that deliver a precise electrical representation of the object’s position.

Figure 1 Pencil drawing illustrates the cutaway view of a basic LVDT. Source: Author

The primary winding is shown at the center of the LVDT. Two secondary coils are wound symmetrically on either side of the primary coil for short-stroke LVDTs, or concentrically over the primary coil for long-stroke versions. The two secondary windings are typically connected in a series-opposed (differential) configuration, ensuring that the output signal accurately reflects the core’s displacement.

In operation, the LVDT’s primary winding is energized by an alternating current of suitable amplitude and frequency, referred to as the primary excitation. The resulting electrical output is a differential AC voltage between the two secondary windings, which varies with the axial position of the core inside the coil. To make this signal more practical, it’s typically converted by electronic circuitry into a higher-level DC voltage or current.

Note that the two secondary windings are connected in series but wound in opposite directions—a series-opposed configuration that ensures the differential output accurately reflects the core’s displacement. For most industrial LVDTs, datasheets assume this series-opposed arrangement. In high-reliability sectors, however, the windings are often kept separate to enable more advanced error-correction techniques.

Figure 2 Schematic illustrates the circuit model of a basic LVDT. Source: Author

How an LVDT works: The silent precision of linear sensing

An LVDT operates as a specialized transformer with a single primary winding, two identical secondary windings, and a movable ferromagnetic core. The core slides axially within the assembly, linked to the monitored component by a push rod. When the primary winding is energized, its magnetic field induces voltages in the secondary coils.

Because these coils are connected in series-opposition, the output is zero when the core is centered at the null position. As the core shifts, magnetic coupling increases in one secondary and decreases in the other, producing a differential output voltage proportional to displacement. The direction of movement is indicated by the phase of the output signal relative to the primary excitation.

To summarize, an AC LVDT is a variable-reluctance transducer that operates by energizing a primary coil with a constant AC supply to induce a magnetic field. This flux is coupled through a moving armature to two secondary coils which are wired in series opposition.

When the armature is at the null position (dead center), the magnetic flux is distributed equally between both secondaries, causing their output to cancel out perfectly. However, as the shaft shifts closer to one coil, the energy in that coil increases relative to the other; by measuring this differential output, the sensor precisely determines the shaft’s position and direction within the tube at all times.

Figure 3 LVDT AC miniature free core position sensors monitor and track the linear motion or position of a target. Source: HGSI

LVDT mechanical configurations: The art of precision displacement

Modern LVDTs are available in three primary mechanical configurations to suit different integration needs. Free (unguided) armature LVDTs utilize a core that moves freely within the bore; because the core is not physically restrained by the sensor, it’s ideal for high-speed dynamic applications where near-zero friction is required.

Captive (guided) armature LVDTs feature internal bearings to maintain core alignment, preventing lateral wear and simplifying installation in industrial automation. Finally, spring-loaded (forced) LVDTs employ an internal spring to maintain contact with the specimen, making them the go-to choice for gauging and QC applications where a permanent mechanical link to the test object is not possible.

Each variety offers a unique trade-off between mechanical simplicity and operational precision.

LVDTs: Key features and operational benefits

LVDT stands as a premier electromechanical transducer, primarily distinguished by its friction-free operation; since the movable core does not touch the coil assembly, there is zero mechanical wear, ensuring an exceptionally long lifetime and high reliability. This physical decoupling allows for infinite resolution, enabling the detection of sub-micron displacements limited only by the signal-conditioning electronics, while its single axis sensitivity ensures that cross-axial movements do not interfere with measurement accuracy.

The design’s separable coil and core mechanism simplifies integration into complex machinery, and its environmentally robust construction allows it to thrive in extreme temperatures or high-vibration settings. Furthermore, LVDT provides remarkable null point stability for consistent zero-referencing, a fast dynamic response capable of tracking rapid changes in position. And, most importantly, an absolute output that retains the correct position data immediately upon power-up, even if the core moved while the system was inactive.

Support electronics: From AC physics to DC simplicity

Although an LVDT is technically a transformer, it requires specialized AC excitation—typically a few volts RMS at several kilohertz—rather than standard line power. Supplying this excitation is a key role of LVDT signal-conditioning electronics, which also convert low-level AC outputs into high-level DC signals, decode directional information from the 180° phase shift at the null point, and provide precise electrical zero adjustments.

These electronics are available in multiple forms, from chip-level components for OEM integration to modular boards and full laboratory instruments. Some LVDTs incorporate integral electronics for simplified “DC-in, DC-out” operation, but such self-contained units may be unsuitable for extreme environments where heat, vibration, or space constraints can compromise internal circuitry.

Figure 4 Datasheet excerpt shows an in-line amplifier transforming LVDT displacement into a directly proportional DC signal. Source: MTS

As a quick side note, LVDT is the technical acronym for the component itself—linear variable differential transformer—while LVDT displacement sensor is often used in industrial catalogs to distinguish it from other measurement technologies, such as capacitive or eddy-current sensors. This descriptive naming helps engineers quickly identify the device’s primary function as a tool for tracking linear position, even though both terms refer to the same electromagnetic hardware.

LVDT signal conditioning: Differential vs. ratiometric

Back to the signals, when choosing how to process an LVDT signal, the main difference lies in how the system handles fluctuations in power. Differential input AC signal conditioning is the more straightforward approach, where the sensor simply measures the difference in voltage between its two secondary coils to determine position. While effective, it has a notable weakness: if the input power (excitation voltage) fluctuates even slightly, the output reading will change as well, potentially leading to errors.

Ratiometric signal conditioning is a more sophisticated method designed to eliminate those errors. Instead of just looking at the difference between the coils, it compares that difference to the total sum of the voltage from both coils. By calculating this ratio, the system automatically cancels out any inconsistencies in the power supply. This makes ratiometric conditioning much more stable and reliable for high-precision tasks, as the measurement stays accurate even if the environment gets hot or the input voltage drifts.

Figure 5 This standalone universal LVDT signal conditioner supports any LVDT sensor with 4, 5, or 6 wires, as well as RVDT or 3-wire half-bridge sensors. Source: Lab Systems

It’s worth noting that an LVDT measures straight-line (linear) displacement, whereas a rotary variable differential transformer (RVDT) measures angular (rotary) displacement.

Precision in motion—From floor to space

From machine tools and robotics to aerospace and space-borne instrumentation, LVDTs remain the gold standard for precise, contactless position measurement. Their rugged construction, infinite resolution, and immunity to wear make them indispensable wherever accuracy must endure vibration, temperature extremes, or time itself.

For today’s makers and engineers, the story doesn’t end at the sensor—modern signal-conditioning ICs such as Analog Devices’ AD598 and Texas Instruments’ PGA970 bring excitation, demodulation, and calibration into compact solutions, while discrete signal-handling circuits continue to offer flexibility for custom designs and extreme environments.

And for those who venture into DIY territory, crafting the coil-core assembly demands patience, precision winding, and careful alignment—skills that embody the very discipline of engineering. Whether you’re building a robotic actuator, a precision test rig, or a satellite mechanism, LVDT proves that elegant physics and smart electronics can turn motion into measurable insight.

I’m ready for a larger displacement of detail in future posts, but at this time, that’s all. Take this knowledge forward—experiment, prototype, and innovate with LVDTs, integrated ICs, discrete signal handlers, and even your own hand-built coil assemblies. Push your designs from the lab bench to the factory floor, and even to orbit. The next breakthrough in precision engineering could start with your hands.

T. K. Hareendran is a self-taught electronics enthusiast with a strong passion for innovative circuit design and hands-on technology. He develops both experimental and practical electronic projects, documenting and sharing his work to support fellow tinkerers and learners. Beyond the workbench, he dedicates time to technical writing and hardware evaluations to contribute meaningfully to the maker community.

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• Magnetostrictive sensors can be replaced with NewTek AC-LVDTs in power generation applications

The post Precision in motion: The engineering value of LVDTs appeared first on EDN.

Worldwide end-user spending on AI models and platforms is projected to total $64 billion in 2026, up 63.4% from $39 billion in 2025, according to Gartner, Inc., a business and technology insights company. Spending on GenAI models is forecast to grow 117%, while AI platform spending will rise 36.9% in 2026.

“Enterprise AI budgets are coming under greater scrutiny, with increased focus on usage efficiency, cost control and measurable outcomes,” said Arunasree Cheparthi, Sr Principal Research Analyst at Gartner. “Spending is shifting toward providers who can demonstrate clear value across cost, latency, performance and reliability.

“This is giving an edge to providers that embed evaluation, cost transparency and usage tracking into customer workflows, making it easier to manage and optimize AI use. However, as spending becomes more usage-driven, providers face increasing pressure to demonstrate real adoption, sustained use and durable margins.”

This dynamic is accelerating growth in domain-specific language models (DSLMs) and specialized models, which are forecast to grow 210% in 2026 (see Table 1).

Table 1: Worldwide AI Platforms and Models End-User Spending Forecast, 2025-2026, (Millions of U.S. Dollars)

			2025-2026 Growth %)
Segment	2025	2026
Foundation Generative AI Models	11,438	23,356	104.2
DSLMs and Specialized GenAI Models	1,583	4,910	210
AI Application Development Platforms	6,885	9,541	38.6
AI Platforms for Data Science and Machine Learning	19,405	26,444	36.3
Total Market	39,311	64,252	63.4

 

“Over the long-term, the biggest winners will be vendors that help enterprises manage where and how AI is used across the business,” said Cheparthi. “As more models enter the market and usage-based pricing becomes harder to predict, buyers will turn to platforms that help them choose the right tools, monitor performance, enforce policy and keep costs under control.”

 

The post Gartner Forecasts Worldwide AI Platforms and Models Market to Grow 63% in 2026 Biggest Winners Will Be Vendors That Help Enterprises Manage Where and How AI is Used appeared first on ELE Times.

Bluetooth and Wi-Fi combine to untether smartphone-to-car connections…at least until the former’s battery drains, that is.

At this year’s beginning, EDN published my overview and hands-on impressions of Apple’s CarPlay and Google’s Android Auto standards, which enable a vehicle radio or automotive head unit to be a display and controller for an iOS or Android device, respectively. Historically, both protocols leveraged a wired USB-based interface between mobile device and vehicle to accomplish the integration objective, for latency, bandwidth and interference-prevention reasons. Newer vehicle models switch to Bluetooth (for initial discovery and connection) and Wi-Fi (for ongoing transmission) communication, enhancing convenience. And bridging these two approaches are wireless adapters that mate an untethered phone to the tethered vehicle.

At the end of that prior coverage, I wrote, “FYI, I’ve also got two single-protocol wireless adapter candidates sitting in my teardown pile awaiting attention.”

Today I’ll analyze the insides of a CarPlay wireless adapter, albeit not the “unit from the Luckymore Store” that I initially planned on disassembling. Next month, I plan to take apart a wireless adapter that implements the Android Auto protocol. And I’ll subsequently wrap up my dissections with a dual-protocol wireless adapter functionally akin to the one still in use in my wife’s Land Rover.

Supplier disappearance and switcheroo

Here again is the wireless CarPlay adapter I’d originally intended as today’s patient.

Right now, as I write these words, it lists for $44.17 on Amazon’s website. But when I bought it in mid-December, it was on sale for $2.99 plus tax, with free shipping. Although the merchant subsequently reported to both Amazon and me that it had shipped, it never actually arrived. And I wasn’t the only one that had this same underwhelming seller experience. Yes, I got my $3.12 back eventually.

I subsequently picked up this WOLIOS adapter (which apparently also came in white), well-reviewed but seemingly no longer available for sale as I write this, from Amazon’s Warehouse-now-Resale section for $11.85 in late February.

Gotta love these conceptual teardown images. And no, I don’t definitively know what “5G” means, either, although I suspect they’re referencing “5 GHz” Wi-Fi.

And, wrapping up the “stock content” suite, a promo video (which, alas, I can’t figure out how to embed) can be found here.

Overview introductions

Now for some real-life photos, as usual accompanied by a 0.75″ (19.1 mm) diameter U.S. penny for size comparison purposes, and of the product packaging first:

Next, what’s inside:

The red disc is an optional sticker for adhering the wireless adapter to the vehicle interior. The included USB-A (female) to USB-C (male) adapter for newer-vehicle use is a nice touch:

And now for our patient:

The hole in the center allows the status LED inside to shine through, as conceptually shown in the prior “stock” images. And the seam around the rim? I’m betting that’s our path inside.

Stubborn adhesive

Speaking of paths to the insides, let’s dive in.

Abundant exposure to my wife’s hair dryer on “high” (both temperature and fan speed) helped, but only a little. I resisted using my heat gun as it might have been melt-inducing overkill.

The last bit of the panel stubbornly refused to dismantle, so I eventually turned to a cutter tool.

Finally free, courtesy of a needle-nose pliers, albeit resulting in PCB mars.

This side’s much more interesting:

The embedded antenna at upper right is curious. As previously noted, two wireless broadcast links are necessary: 2.4 GHz Bluetooth for initial setup, and Wi-Fi for ongoing streaming. The latter is usually based on the 5 GHz band for higher transfer bandwidth, since range isn’t a concern in a diminutive vehicle interior. But I only see one antenna here.

Did the manufacturer figure out some way to passably enable it to support both bands? Or is there another one somewhere that I’m overlooking? Or does this particular design, in contrast to “Wireless Compability [sic]: 5.8 GHz Radio Frequency” spec claims to the contrary, leverage 2.4 GHz Wi-Fi instead? Or…???

Equally baffling is the user-inaccessible switch in the lower left corner (with the status LED to its right, in the middle of the lower edge). What’s the switch for? And then there’s the unmarked square IC enigma above and to the switch’s right (and LED’s left). I assume it’s the application processor, and that it runs at 24 MHz (courtesy of the marked oscillator to its left).

But who makes it, and what’s its specific product code? Another mystery. These are commonly Arm-based, with Chinese fabless suppliers Allwinner and Rockchip common sources (so says Google AI Overviews, at least), although this reverse-engineered unit from early and ongoing-leading adapter supplier Carlinkit reportedly leverages a Freescale (now NXP Semiconductors) SoC, the i.MX6 UltraLite.

To its right, and to the left of (and slightly below) the aforementioned antenna(e) assembly, is a Winbond W25Q128 128 Mbit serial flash memory, presumably housing the user-upgradeable system firmware image. And then there’s the even larger shiny-shield covered square IC below the antenna(e). This one’s easier to figure out, thanks to the careful application of a flat-head screwdriver acting as a lever.

It’s Realtek’s RTL8733, supporting the device’s wireless subsystem. The RTL8733 comprehends both Bluetooth and dual-band Wi-Fi transceiver functionality and presumably operates in conjunction with the mystery application processor to implement the adapter’s wireless-to-USB wired bridge functionality.

That’s all I’ve got for you today, folks. As always, let loose with your thoughts in the comments!

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

Related Content

• Apple CarPlay and Google Android Auto: Usage impressions and manufacturer tensions
• Spotify’s Car Thing: “Why does it exist” is the crux of the questioning
• Amazon’s Echo Auto Assistant: Legacy vehicle retrofit-relevant

The post Adapter makes CarPlay connectivity wireless appeared first on EDN.

The second phase of the India Semiconductor Mission has received a much larger budget than the first phase, with Rs 1.27 lakh crore allocated to support different areas of semiconductor and electronics manufacturing. Similar to the first phase, it is expected that most of the budget will be allocated to the selected individual projects, either as a capital subsidy (in which case, the government might contribute less than the 50% that it agreed to in the first phase) and as manufacturing-linked incentives paid on a per-unit basis once sales are completed. An added element of Incremental incentive boosters will be made available for those manufacturing those electronic goods that make use of domestic technologies and components.

It promises to extend the government’s stated intent of making India a strategic hub for the electronics value chain and build those capabilities in-house, with both intellectual and human capital, when only a few countries globally command such capabilities across different components of the value chain.

The government had stated from the very beginning that this would be a multi- decade process and its continued patronage with a more substantive kitty of money is indeed welcome. While this might not be a high job-generating industry per se, it is their strategic, geo-political considerations to deploy public funds. Whether this was a good initial wager is still largely unclear, as many of the first round of awarded projects have still to commence commercial production.

Some technology is well beyond the reach of all economies: The complexity of the machines that print nanoscale feature images from “extreme ultraviolet,” or EUV, lithography, currently mastered only by the Netherlands with a looming challenge
from Japan, means the thought of India’s committing investment to such a technology is enticing, but unfathomable. Such strategic capabilities have hard payoffs. Such a capacity to do “hard things” will breed resistance to India rapidly developing the capability and attracting talent.

 

The post A New Wave of Chip Making, Phase 2 on the India Semiconductor Mission appeared first on ELE Times.

Tata Electronics, a group company under the $103 billion Tata Sons conglomerate, is set to establish the country’s first commercial chip fabrication plant utilising an older technology to focus on a market segment for semiconductor designs from external customers, said an official with knowledge of the project. This would mark the first time the Indian government allocates resources to support establishing a chip making unit. The Tata Group had earlier reported the creation of India’s maiden integrated device manufacturer (IDM) in the country, a unit that designs and produces semiconductor chips in-house.

Tata Electronics Pvt. It will use technology far older than planned to produce India’s first semiconductor wafers, coming from a country with virtually no history of the sophisticated technology, highlighting how much work it will need to do to catch up. The tech unit of the sprawling Tata conglomerate will use 90-nanometer process technology for a large part of its first chip fab, which will be located in Dholera, a city in the western state of Gujarat, according to the sources.

That’s a very mature technology used in low-end industrial applications and cars – one that might even be obsolete in the coming years. It’s a more humble beginning compared with the 28nm node which Tata Sons Pvt., the group’s holding company, said would be the first step of its chipmaking innings in its annual report for the year ended March 2025.
It has been observed that Tata’s public plans could have been aspirational regarding what is realistically possible on the ground in the next couple of years. Tata’s entry into chipmaking involves a partnership with Taiwanese rival Powerchip Semiconductor Manufacturing Corp.

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