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The automotive industry is undergoing a fundamental transformation. Vehicles are no longer static machines defined at production. They are becoming dynamic, software-defined platforms that evolve over time through updates, new features, and continuous improvements.

This shift is changing the role of semiconductors. What was once a supporting function is now central to how vehicles operate, differentiate, and deliver value. As software increasingly defines the vehicle experience, compute and power architectures must support far more than fixed functionality.

By the next decade, software-defined vehicle (SDV) architectures are expected to dominate new vehicle platforms. Automakers are investing heavily to move toward systems that can adapt over long lifecycles, even as software and AI evolve at a much faster pace.

The result is a new set of challenges that go beyond incremental improvements in performance.

A growing mismatch between lifecycles

At the core of the SDV transition is a structural mismatch.

While vehicles must operate safely and reliably for more than a decade, software does not follow the same timeline. New capabilities are introduced continuously—through AI model updates, over-the-air (OTA) features, and evolving applications that extend beyond the original vehicle design.

This creates a system that operates on multiple timelines at once. Safety-critical control systems require stability and certification, while AI-driven functions demand flexibility and rapid iteration. Traditional architectures struggle to accommodate both.

The conventional model, built around tightly coupled hardware and software and distributed electronic control units (ECUs), cannot scale to this level of complexity. Even as industry transitions toward centralized and zonal architectures, the underlying challenge remains: how to support continuous evolution without increasing risk.

Compute is now a system-level challenge

At the same time, the demand for in-vehicle compute is increasing dramatically.

Advanced driver assistance, higher levels of autonomy, and AI-driven experiences all require high-performance processing at the edge. These workloads must operate within strict constraints—limited power, tight thermal envelopes, and automotive-grade reliability.

Monolithic system-on-chip (SoC) designs make it difficult to balance these competing demands. A single device must meet performance, cost, safety, and lifecycle requirements simultaneously, which introduces inefficiencies and limits flexibility. As a result, compute is no longer a component decision. It’s a system-level problem that affects how the entire vehicle is designed and evolves over time.

Moving toward heterogeneous and modular architectures

The industry is beginning to respond by shifting toward more flexible architectures.

Instead of integrating all functionality into a single chip, new designs increasingly rely on heterogeneous systems that combine multiple compute elements—CPUs, GPUs, and AI accelerators—working together. This approach allows different parts of the system to be optimized independently while still functioning as a unified platform.

More importantly, it enables alignment with real-world requirements. Safety-critical functions can rely on mature, well-understood technologies, while AI workloads can take advantage of leading-edge processing. Memory, connectivity, and I/O can be placed where they deliver the best efficiency.

This shift reflects a broader transition from optimizing individual components to designing systems that balance performance, cost, and lifecycle considerations.

This system-level evolution is already visible in current automotive compute platforms.

High-performance SoC families such as R‑Car illustrate how architectures are adapting to SDV requirements. These platforms bring together heterogeneous compute, safety capabilities, and efficient power management in a scalable framework that can be deployed across different vehicle domains.

They are designed not only for central compute in ADAS and autonomous applications, but also to integrate with zonal controllers and broader vehicle systems. This enables automakers to build platforms that can evolve over time, rather than redesigning from scratch for each new generation.

The key point is not peak performance alone. It’s the ability to deliver consistent, predictable behavior across a wide range of use cases and over long operational lifetimes.

Supporting diverse OEM strategies

The transition to software-defined vehicles is not uniform across the industry.

Some automakers are moving toward fully centralized architectures, while others are adopting hybrid or zonal approaches. Different strategies reflect different priorities, including cost structure, time-to-market, and control over software ecosystems.

This diversity requires flexibility. Suppliers must support multiple architectural paths and allow automakers to make trade-offs that fit their specific goals. An open, scalable approach becomes increasingly important as vehicles evolve from isolated products to connected, long-lifecycle platforms.

AI is accelerating the need for change

Artificial intelligence is amplifying these challenges.

Early automotive AI focused on discrete functions such as perception. Today, vehicles must handle multiple AI-driven workloads simultaneously, from sensor fusion to planning to in-cabin interactions. These systems must operate in real time while meeting strict safety requirements.

This shifts the focus away from simplified performance metrics toward broader system considerations. Latency, determinism, power efficiency, and data movement all become critical. Supporting AI at scale requires architectures that can orchestrate diverse workloads efficiently while maintaining predictable performance. This reinforces the need for heterogeneous, system-level design.

From products to platforms

In other words, as complexity increases, the industry is moving toward integrated platforms.

Automakers are no longer looking solely for components. They are looking for solutions that combine hardware, software, and development ecosystems in a way that reduces integration risk and accelerates deployment.

This shift reflects a broader change in the semiconductor industry—from delivering individual devices to enabling complete system solutions. And this transition to software-defined vehicles is a long-term shift that will unfold over the next decade.

What is already clear is that success will depend on the ability to design systems that balance long-term reliability with rapid innovation. This requires new thinking—not just in silicon, but in architecture, development processes, and ecosystem collaboration.

The industry is moving beyond optimizing individual parts. It’s designing vehicles as cohesive, adaptable systems. And compute sits at the center of that transformation.

Vivek Bhan is senior VP and GM of high-performance computing at Renesas Electronics.

Related Content

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• Why the Cloud Is Essential for SDV Development
• Unveiling the Transformation of Software-Defined Vehicles
• Software-defined vehicle (SDV): A technology to watch in 2025
• Taming the Increasing Complexity of the Software-Defined Vehicle

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Aehr Test Systems of Fremont, CA, USA — which provides solutions for testing, burning-in and stabilizing semiconductor devices in wafer-level, singulated die, and packaged-part form — has received an additional follow-on production order from its lead silicon photonics customer for a fully automated FOX-XP wafer-level burn-in (WLBI) system. The system will support high-volume production burn-in of silicon photonics devices used in AI optical interconnect and hyperscale data-center applications...

Molecular beam epitaxy (MBE) system maker Riber S.A. of Bezons, France has received a new order from 3SP Technologies S.A.S. of Nozay, Essonne, France — a long-standing customer for more than 20 years — for an industrial passivation platform. The system is scheduled for delivery in 2027...

📈 КПІ ім. Ігоря Сікорського — серед лідерів української вищої освіти у Webometrics 2026 KPI4U-2 пт, 07/10/2026 - 10:26

Нові перемоги на ICOA 2026: як КПІ ім. Ігоря Сікорського готує кіберчемпіонів kpi пт, 07/10/2026 - 10:19

I like going through the electronics trash bins at my college, here's some stuff i found today. The second item is a speaker part, the ring is super magnetic it was a challenge to pry it apart! submitted by /u/SwabluOnACloud [link] [comments]

submitted by /u/OkParsley6142 [link] [comments]

Закріплення освітніх програм за структурними підрозділами університету kpi чт, 07/09/2026 - 17:20

📰 Газета "Київський політехнік" № 25-26 за 2026 (.pdf) Інформація КП чт, 07/09/2026 - 16:45

Нове життя лабораторного обладнання: міжнародна підтримка електрохімічної освіти в КПІ kpi чт, 07/09/2026 - 15:58

A traditional Newton's Cradle only swings for a few seconds before friction stops it. I wanted to build one that keeps swinging continuously while still looking completely normal. The system uses an ESP32-C3, an inductive proximity sensor, and an electromagnet to replace only the energy lost during each swing. I'd be happy to answer any questions about the design! submitted by /u/Objective_Net_1378 [link] [comments]

Revisiting the past can leave the reader with a range of reactions, including both bemusement at then-embryonic developments and amazement at the accuracy of forecast extrapolations.

After reading the sentence that follows this one, pause for a moment and guess when it was first written, prior to plunging forward in my own prose:

The amount of effort and money put into artificial intelligence has been quite limited, and there are only about a half-dozen major centers of such activity in the world.

Clearly, this quote is a “few” years old! Consider, for example, that last September Gartner forecasted that worldwide spending on AI would hit $1.5 trillion for that (last) year. The above quote is from renowned astrophysicist Carl Sagan’s treatise, “Broca’s Brain: Reflections on the Romance of Science”, first published in 1979, with which I recently reconnected over a long weekend read.

Most people overestimate what they can achieve in one year and underestimate what they can achieve in ten years. (Bill Gates)

Specifically, it came from chapter 20, “In Defense of Robots”, which in its original form was titled “In Praise of Robots” and appeared in the January 1975 edition of Natural History magazine. Unsurprisingly, given that the source material is more than a half-century old at this point, some of it is charmingly dated. Consider, for example, this chapter excerpt:

There will be strong pressures for continued miniaturization of intelligent machines. It is clear that remarkable miniaturization has already occurred. Vacuum tubes have been replaced by transistors, wired circuits by printed circuit boards, and entire computer systems by silicon chip microcircuitry. Today, a circuit that used to occupy much of a 1930 radio set can be printed on the tip of a pin.

Or, speaking of the current state of intelligent machines, this passage:

The ten best chess players in the world still have nothing to fear from any present computer, but the situation is changing. Recently, a computer for the first time did well enough to enter the Minnesota State Chess Open. This may be the first time that a non-human has entered a major sporting event on the planet Earth…The computer did not win the chess open, but this is the first time one has done well enough to enter such a competition. Chess playing computers are improving extremely rapidly.

And then there’s this, focusing on Sagan’s primary area of expertise, space:

In the exploration of Mars, unmanned vehicles have already soft-landed, and only a little further in the future they will roam about the surface of the Red Planet as some now do on the Moon.

What would Sagan have thought about the fact that, as I’m writing these words, NASA just announced that its Perseverance rover has traveled the distance of a marathon on Mars, notably much of it autonomously? He wouldn’t, I’d argue, be at all surprised. And that, dear readers, is at the core of why I’m focusing on his book, and this chapter in particular, today. To wit, immediately after the prior quote, he elaborated on his prognostication “tease”, writing:

The Viking landers deposited on Mars in summer of 1976 have a very interesting array of sensors and scientific instruments, which are the extension of human senses to an alien environment. The obvious post-Viking device for Martian exploration, one which takes advantage of the Viking technology, is a Viking rover in which the equivalent of an entire Viking spacecraft, but with considerably improved science, is put on wheels or tractor treads and permitted to rove slowly over the Martian landscape.

But now we have a new problem, one that is never encountered in machine operation on the Earth’s surface.  Although Mars is the second closest planet, it is so far from the Earth that light travel becomes significant. At a typical relative position of Mars and the Earth, the planet is 20 light minutes away. Thus, if the spacecraft were confronted with a steep incline, it might send a message of inquiry back to Earth. Forty minutes later, the response would arrive saying something like, “For heaven’s sake, stand dead still!” But by then, of course, an unsophisticated machine would have tumbled into a gully.

Consequently, any Martian rover requires slope and roughness sensors. Fortunately, these are readily available and are even seen in some children’s toys. When confronted with a precipitous slope or large boulder, the spacecraft would either stop until receiving instructions from the Earth in response to its query and televised picture of the terrain, or back off and start in another and safer direction. Much more elaborate contingency decision networks can be built into the onboard computers of spacecraft of the 1980s.

Any sufficiently advanced technology no longer distinguishes itself from pure magic. (Arthur C. Clarke)

The fundamental point of In Defense of Robots, at least per my interpretation of it, is to provide Sagan with a platform to answer a question he posited at the beginning:

The powerful abilities of computing machines to do arithmetic hundreds of millions of times faster than unaided human beings are legendary. But what about really difficult matters? Can machines in any sense think through a new problem? Can they make discussions of the branch-contingency-tree variety with which we think of as characteristically human?

Sagan’s answer to that question was an unqualified “yes”, and here’s what he thought it would look like, again specific to astrophysics and related topics:

In the development of such machines we find a kind of convergent evolution. Viking is, in a curious sense, like some great outsized clumsily constructed insect. It is not yet ambulatory and is certainly incapable of self-reproduction, but it has an exoskeleton, it has a wide range of insect-like sensory organs, and it is about as intelligent as a dragonfly.

But Viking has some advantages that insects do not. It can, on occasion, by inquiring of its controllers on Earth, assume the intelligence of a human being. The controllers are able to reprogram the Viking computer on the basis of the decisions they make.

As the field of machine intelligence advances, and as increasingly distant objects in the solar system become accessible to exploration, we will see the development of increasingly sophisticated onboard computers, slowly climbing the phylogenetic tree from insect intelligence to crocodile intelligence to squirrel intelligence and, in the not very remote future, I think, to dog intelligence.

That said, Sagan was also keen to expand far beyond astrophysics with his forecasts, even to the realm of psychoanalysis. Consider chatbots’ increasingly common use as virtual therapists, albeit with diverse user experiences and outcomes, as you read the following excerpt:

In a time when more and more people in our society seem to be in need of psychiatric counseling, and when timesharing of computers is widespread, I can even imagine the development of a network of computer psychotherapeutic terminals something like arrays of large telephone booths in which for a few dollars a session we are able to talk to an attentive tested and largely non-directive psychotherapist. Ensuring the confidentiality of the psychiatric dialogue is one of the several important steps still to be worked out.

Or consider something a bit “closer to home” for the broad engineering community, that of humanoid and other robotic systems for industrial and other related applications:

If intelligent machines for terrestrial mining and space exploratory applications are pursued, the time cannot be far off when household and other domestic robots will become commercially feasible…There are many common tasks, ranging from bartending to floor washing, that involve a very limited array of intellectual capabilities, albeit substantial stamina and patience.

All-purpose ambulatory household robots, which perform domestic functions as well as a proper 19th century butler, are probably many decades off, but more specialized machines, each adapted to specific household functions, are probably already on the horizon. It is possible to imagine many other civic tasks and essential functions of everyday life carried out by intelligent machines.

Much in life is simply a matter of perspective. It’s not inherently good or bad, a success or failure; it’s how we choose to look at things that makes the difference. (David Niven)

But I can’t help but wonder: was Sagan too sanguine about the societal upheaval caused by AI-powered robotic (and broader AI) supplant?

For the development of domestic and civic robots to be a general civic good, the effect of re-employment of those human beings displaced by the robots must be of course arranged. But over a human generation, that should not be too difficult, particularly if there are enlightened educational reforms. Human beings enjoy learning.

If anything, he seemed more concerned that human beings’ overreaction (at least in his eyes) to such displacement might unnecessarily delay or even preclude this transition and broader transformation, to the broader detriment of our species (thereby at least in part explaining, I suspect, the shift from robot “praise” to “defense” from the 1975 article to 1979 book chapter):

We appear to be on the verge of developing a wide variety of intelligent machines capable of performing tasks too dangerous, too expensive, too onerous, or too boring for human beings. The development of such machines is, in my mind, one of the few legitimate spin-offs of the space program. The efficient exploitation of energy and agriculture, upon which our survival as a species depends, may even be contingent on the development of such machines.

The main obstacle seems to be a very human problem, the quiet feeling that comes stealthily and unbidden, and argues that there is something threatening or inhuman about machines performing tasks as well or better than human beings, or a sense of loathing for creatures made of silicon and germanium rather than proteins and nucleic acids. But in many respects, our survival as a species depends on our transcending such primitive chauvinisms.

In part, our adjustment to intelligent machines is a matter of acclimatization. There are already cardiac pacemakers that can sense the beat of a human heart. Only when there is the slightest hint of fibrillation does the pacemaker stimulate the heart. This is a mild but very useful sort of machine intelligence. I cannot imagine the wearer of this device resenting its intelligence [EDITOR NOTE: as regular readers will likely already understand, I particularly resonated with this point].

I think in a relatively short period of time there will be a very similar sort of acceptance for much more intelligent and sophisticated machines. There is nothing inhuman about an intelligent machine. It is indeed an expression of those superb intellectual qualities that only human beings, of all creatures on this planet, now possess.

Whether or not you resonate with Sagan’s perspectives in the excerpts I’ve shared, I suspect you’ll (near-)universally agree with my admiration for the accuracy of his prophecies, along with the rare combination of intelligence and open-mindedness (with at least one notable exception) that were at their foundation. Regardless, I encourage you to pick up a copy of Broca’s Brain: Reflections on the Romance of Science and give it a read for yourself.

It’s only $6.99 on Kindle as I write this (and as I read it), and I also commonly come across both hardcover and paperback copies of it at used bookstores. There’s always also your public library, of course. And worst case, I stumbled across a YouTube video of someone reading the (bulk of the) text of the In Praise of Robots chapter:

Fair warning: there’s at least one several-paragraph section missing (I suspect due to a multi-“take” merging edit error, not intentionally), ironically the one from which the quote that opened this writeup came. And the regularly changing “psychedelic” special effects (which I suspect were an attempt, apparently successfully, to circumvent copyright infringement algorithms) compel me to encourage you to focus solely on the audio. But, hey…free!

Regardless of how you end up consuming Broca’s Brain, I hope you find it a fruitful experience, versus a waste of time. Be sure to come back here afterward and share your thoughts in the comments, ok? Thanks!

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

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🏆 7 PhD-дослідницьких проєктів КПІ ім. Ігоря Сікорського стали переможцями конкурсного добору МОН України! KPI4U-2 чт, 07/09/2026 - 12:40

Optical in-situ metrology system maker LayTec AG of Berlin, Germany says that Sandia National Laboratories has purchased a customized manual-loading version of its EpiX mapping station, combining white light reflectance (WLR) and photoluminescence (PL) metrology for non-contact optical wafer mapping...

Regarding the patent infringement complaint filed against it by Wolfspeed Inc of Durham, NC, USA — which makes silicon carbide (SiC) materials and power semiconductor devices — in the United States District Court for the District of Delaware, gallium nitride (GaN) power IC and silicon carbide (SiC) technology firm Navitas Semiconductor Corp of Torrance, CA, USA says that it disputes the allegations, will “vigorously defend itself and its products against baseless accusations of infringement”, and expects to prevail in the litigation. In the meantime, Navitas remains “fully committed to executing its growth strategy and delivering innovative products that address the rapidly expanding demand for next-generation power semiconductors”...

Samsung’s PM1763 PCIe 6.0-based enterprise SSD features 9th-generation V-NAND flash memory and a new 4-nm controller. Optimized for AI and HPC servers, the drive is available in 4-TB, 8-TB, and 16-TB capacities. The 16-TB model delivers sequential read and write speeds of up to 28,400 MB/s and 21,900 MB/s, respectively—up to twice the performance of its predecessor, the PM1753.

According to the company, the PM1763 can transfer a 40-GB LLM in approximately 1.4 seconds, helping minimize data latency between processors and accelerators while improving overall AI processing efficiency. The SSD is optimized for liquid-cooled server environments through direct-to-chip cooling. This design enables sustained peak performance while improving power efficiency by up to 1.8 times compared to the previous generation.

To address security requirements for AI and virtualized infrastructure, the PM1763 supports post-quantum cryptography (PQC), the Security Protocol and Data Model (SPDM) 1.4, and Commercial National Security Algorithm (CNSA) 2.0. It also provides link encryption based on the TEE Device Interface Security Protocol (TDISP) to reinforce data protection across storage interfaces.

Samsung has now begun mass production of the PM1763 SSD.

PM1763 product page 

Samsung Electronics  

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Using a vertically stacked-die design, AOS’s DFN6×5 AmpStack package integrates two MOSFETs configured as a high-side/low-side half-bridge. It increases power density and maximizes available PCB space compared to a solution using two discrete DFN5×6 MOSFETs. The package enables high-density power conversion applications ranging from megawatt AI factories to power tools.

The AOPL66801 80-V MOSFET showcases the new half-bridge package with an optimized switch-node clip connecting the high-side and low-side MOSFETs. This architecture minimizes parasitic inductance within the package. Compared to a standard discrete solution, it also reduces PCB parasitic inductance, minimizing phase-node voltage ringing and decreasing stress on the MOSFETs. Key specifications for the AOPL66801 include:

An integrated Kelvin sense pin maintains gate-voltage stability during high di/dt switching. The dedicated connection provides a more effective high-side gate-drive path, helping reduce switching losses. The device also supports a maximum junction temperature of 175 °C for increased thermal capability.

The AOPL66801 is available now in production quantities with a 16-week lead time. Pricing is $6.16 per unit in 1000-piece quantities.

AOPL66801 product page 

Alpha & Omega Semiconductor 

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The Vishay 34 PHE absolute position sensor provides 10-turn linear or rotary displacement sensing with a 3600° range. Using non-contact Hall-effect technology, it delivers up to ±1% linearity (full stroke), 1° resolution, and a service life of more than 10 million cycles.

According to Vishay, the 34 PHE is priced 40% lower than previous-generation devices. It is designed for servo loop motion control systems requiring high accuracy and long-term stability in harsh environments. Typical applications include industrial motor and actuator displacement tracking, solar panel alignment systems, and flow control valve positioning.

The sensor features IP65 sealing and withstands vibration up to 20 g and shock up to 50 g. Integrated reverse-voltage and overvoltage protection (−14 VDC and +28 VDC) reduces the need for external protection circuitry. It supports single or dual analog ratiometric outputs or a digital PWM output. In dual-output mode, the two channels track position in opposite directions to enable basic fault detection. The 34 PHE reports its position immediately after power-up, even following a power loss, without requiring recalibration, homing, or initialization.

Samples and production quantities of the sensor are available now, with lead times of 14 weeks.

34 PHE product page 

Vishay Intertechnology 

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VeriSilicon’s CPP2000 Camera Post-Processing IP improves image quality for reliable vision performance in robotics, drones, and other mobile vision applications. It is designed for straightforward SoC integration and processes YUV images from image signal processors using a range of image enhancement techniques.

The IP supports image and video processing at up to 8K resolution, applying motion-compensated temporal filtering, advanced spatial noise reduction, chroma adjustment, dynamic contrast enhancement, and edge enhancement to improve noise suppression, sharpness, contrast, and overall detail fidelity.

The CPP2000 is implemented as a modular, streaming hardware pipeline in which each stage operates as a dedicated accelerator, enabling continuous real-time processing from input to output. Multiple hardware configuration options are available to address varying requirements for power, performance, area, and latency across applications.

CPP2000 product page  

VeriSilicon

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