Today during Gamescom, Bethesda announced that it was adding the previously-teased dune buggy to Starfield in a free update that is set to go live later tonight. Bethesda also provided another sneak peek at Shattered Space, the upcoming DLC for Starfield, and revealed that it’s launching September 30 on consoles and…
The new MINIX CA361 and MINIX CP86 CarPlay systems have just been introduced, two connection alternatives for our car’s display. Thanks to the small MINIX CA361 box it ...
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Tsunenobu Kimoto, a professor of electronic science and engineering at Kyoto University, literally wrote the book on silicon carbide technology. Fundamentals of Silicon Carbide Technology, published in 2014, covers properties of SiC materials, processing technology, theory, and analysis of practical devices.
Kimoto, whose silicon carbide research has led to better fabrication techniques, improved the quality of wafers and reduced their defects. His innovations, which made silicon carbide semiconductor devices more efficient and more reliable and thus helped make them commercially viable, have had a significant impact on modern technology.
Employer
Kyoto University
Title
Professor of electronic science and engineering
Member grade
Fellow
Alma mater
Kyoto University
For his contributions to silicon carbide material and power devices, the IEEE Fellow was honored with this year’s IEEE Andrew S. Grove Award, sponsored by the IEEE Electron Devices Society.
Decades before a Tesla Model 3 rolled off the assembly line with an SiC inverter, a small cadre of researchers, including Kimoto, foresaw the promise of silicon carbide technology. In obscurity they studied it and refined the techniques for fabricating power transistors with characteristics superior to those of the silicon devices then in mainstream use.
Today MOSFETs and other silicon carbide transistors greatly reduce on-state loss and switching losses in power-conversion systems, such as the inverters in an electric vehicle used to convert the battery’s direct current to the alternating current that drives the motor. Lower switching losses make the vehicles more efficient, reducing the size and weight of their power electronics and improving power-train performance. Silicon carbide–based chargers, which convert alternating current to direct current, provide similar improvements in efficiency.
But those tools didn’t just appear. “We had to first develop basic techniques such as how to dope the material to make n-type and p-type semiconductor crystals,” Kimoto says. N-type crystals’ atomic structures are arranged so that electrons, with their negative charges, move freely through the material’s lattice. Conversely, the atomic arrangement of p-type crystals’ contains positively charged holes.
Kimoto’s interest in silicon carbide began when he was working on his Ph.D. at Kyoto University in 1990.
“At that time, few people were working on silicon carbide devices,” he says. “And for those who were, the main target for silicon carbide was blue LED.
“There was hardly any interest in silicon carbide power devices, like MOSFETs and Schottky barrier diodes.”
Kimoto began by studying how SiC might be used as the basis of a blue LED. But then he read B. Jayant Baliga’s 1989 paper “Power Semiconductor Device Figure of Merit for High-Frequency Applications” in IEEE Electron Device Letters, and he attended a presentation by Baliga, the 2014 IEEE Medal of Honor recipient, on the topic.
“I was convinced that silicon carbide was very promising for power devices,” Kimoto says. “The problem was that we had no wafers and no substrate material,” without which it was impossible to fabricate the devices commercially.
In order to get silicon carbide power devices, “researchers like myself had to develop basic technology such as how to dope the material to make p-type and n-type crystals,” he says. “There was also the matter of forming high-quality oxides on silicon carbide.” Silicon dioxide is used in a MOSFET to isolate the gate and prevent electrons from flowing into it.
The first challenge Kimoto tackled was producing pure silicon carbide crystals. He decided to start with carborundum, a form of silicon carbide commonly used as an abrasive. Kimoto took some factory waste materials—small crystals of silicon carbide measuring roughly 5 millimeters by 8 mm—and polished them.
He found he had highly doped n-type crystals. But he realized having only highly doped n-type SiC would be of little use in power applications unless he also could produce lightly doped (high purity) n-type and p-type SiC.
Connecting the two material types creates a depletion region straddling the junction where the n-type and p-type sides meet. In this region, the free, mobile charges are lost because of diffusion and recombination with their opposite charges, and an electric field is established that can be exploited to control the flow of charges across the boundary.
“Silicon carbide is a family with many, many brothers.”
By using an established technique, chemical vapor deposition, Kimoto was able to grow high-purity silicon carbide. The technique grows SiC as a layer on a substrate by introducing gasses into a reaction chamber.
At the time, silicon carbide, gallium nitride, and zinc selenide were all contenders in the race to produce a practical blue LED. Silicon carbide, Kimoto says, had only one advantage: It was relatively easy to make a silicon carbide p-n junction. Creating p-n junctions was still difficult to do with the other two options.
By the early 1990s, it was starting to become clear that SiC wasn’t going to win the blue-LED sweepstakes, however. The inescapable reality of the laws of physics trumped the SiC researchers’ belief that they could somehow overcome the material’s inherent properties. SiC has what is known as an indirect band gap structure, so when charge carriers are injected, the probability of the charges recombining and emitting photons is low, leading to poor efficiency as a light source.
While the blue-LED quest was making headlines, many low-profile advances were being made using SiC for power devices. By 1993, a team led by Kimoto and Hiroyuki Matsunami demonstrated the first 1,100-volt silicon carbide Schottky diodes, which they described in a paper in IEEE Electron Device Letters. The diodes produced by the team and others yielded fast switching that was not possible with silicon diodes.
“With silicon p-n diodes,” Kimoto says, “we need about a half microsecond for switching. But with a silicon carbide, it takes only 10 nanoseconds.”
The ability to switch devices on and off rapidly makes power supplies and inverters more efficient because they waste less energy as heat. Higher efficiency and less heat also permit designs that are smaller and lighter. That’s a big deal for electric vehicles, where less weight means less energy consumption.
Kimoto’s second breakthrough was identifying which form of the silicon carbide material would be most useful for electronics applications.
“Silicon carbide is a family with many, many brothers,” Kimoto says, noting that more than 100 variants with different silicon-carbon atomic structures exist.
The 6H-type silicon carbide was the default standard phase used by researchers targeting blue LEDs, but Kimoto discovered that the 4H-type has much better properties for power devices, including high electron mobility. Now all silicon carbide power devices and wafer products are made with the 4H-type.
Silicon carbide power devices in electric vehicles can improve energy efficiency by about 10 percent compared with silicon, Kimoto says. In electric trains, he says, the power required to propel the cars can be cut by 30 percent compared with those using silicon-based power devices.
Challenges remain, he acknowledges. Although silicon carbide power transistors are used in Teslas, other EVs, and electric trains, their performance is still far from ideal because of defects present at the silicon dioxide–SiC interface, he says. The interface defects lower the performance and reliability of MOS-based transistors, so Kimoto and others are working to reduce the defects.
When Kimoto was an only child growing up in Wakayama, Japan, near Osaka, his parents insisted he study medicine, and they expected him to live with them as an adult. His father was a garment factory worker; his mother was a homemaker. His move to Kyoto to study engineering “disappointed them on both counts,” he says.
His interest in engineering was sparked, he recalls, when he was in junior high school, and Japan and the United States were competing for semiconductor industry supremacy.
At Kyoto University, he earned bachelor’s and master’s degrees in electrical engineering, in 1986 and 1988. After graduating, he took a job at Sumitomo Electric Industries’ R&D center in Itami. He worked with silicon-based materials there but wasn’t satisfied with the center’s research opportunities.
He returned to Kyoto University in 1990 to pursue his doctorate. While studying power electronics and high-temperature devices, he also gained an understanding of material defects, breakdown, mobility, and luminescence.
“My experience working at the company was very valuable, but I didn’t want to go back to industry again,” he says. By the time he earned his doctorate in 1996, the university had hired him as a research associate.
He has been there ever since, turning out innovations that have helped make silicon carbide an indispensable part of modern life.
Kimoto joined IEEE in the late 1990s. An active volunteer, he has helped grow the worldwide silicon carbide community.
He is an editor of IEEE Transactions on Electron Devices, and he has served on program committees for conferences including the International Symposium on Power Semiconductor Devices and ICs and the IEEE Workshop on Wide Bandgap Power Devices and Applications.
“Now when we hold a silicon carbide conference, more than 1,000 people gather,” he says. “At IEEE conferences like the International Electron Devices Meeting or ISPSD, we always see several well-attended sessions on silicon carbide power devices because more IEEE members pay attention to this field now.”
Autonomous vehicles (AVs) have made headlines in recent months, though often for all the wrong reasons. Cruise, Waymo, and Tesla are all under U.S. federal investigation for a variety of accidents, some of which caused serious injury or death.
A new paper published in Nature puts numbers to the problem. Its authors analyzed over 37,000 accidents involving autonomous and human-driven vehicles to gauge risk across several accident scenarios. The paper reports AVs were generally less prone to accidents than those driven by humans, but significantly underperformed humans in some situations.
“The conclusion may not be surprising given the technological context,” said Shengxuan Ding, an author on the paper. “However, challenges remain under specific conditions, necessitating advanced algorithms and sensors and updates to infrastructure to effectively support AV technology.”
The paper, authored by two researchers at the University of Central Florida, analyzed data from 2,100 accidents involving advanced driving systems (SAE Level 4) and advanced driver-assistance systems (SAE Level 2) alongside 35,113 accidents involving human-driven vehicles. The study pulled from publicly available data on human-driven vehicle accidents in the state of California and the AVOID autonomous vehicle operation incident dataset, which the authors made public last year.
While the breadth of the paper’s data is significant, the paper’s “matched case-control analysis” is what sets it apart. Autonomous and human-driven vehicles tend to encounter different roads in different conditions, which can skew accident data. The paper categorizes risks by the variables surrounding the accident, such as whether the vehicle was moving straight or turning, and the conditions of the road and weather.
Level 4 self-driving vehicles were roughly 36 percent less likely to be involved in moderate injury accidents and 90 percent less likely to be involved in a fatal accident.
SAE Level 4 self-driving vehicles (those capable of full self-driving without a human at the wheel) performed especially well by several metrics. They were roughly 36 percent less likely to be involved in moderate injury accidents and 90 percent less likely to be involved in a fatal accident. Compared to human-driven vehicles, the risk of rear-end collision was roughly halved, and the risk of a broadside collision was roughly one-fifth. Level 4 AVs were close to one-fifthtieth as likely to run off the road.
The paper’s findings are generally favorable for level 4 AVs, but they perform worse in turns, and at dawn and dusk.Nature
These figures look good for AVs. However, Missy Cummings, director of George Mason University’s Autonomy and Robotics Center and former safety advisor for the National Highway Traffic Safety Administration, was skeptical of the findings.
“The ground rules should be that when you analyze AV accidents, you cannot combine accidents with self-driving cars [SAE Level 4] with the accidents of Teslas [SAE Level 2],” said Cummings. She took issue with discussing them in tandem and points out these categories of vehicles operate differently—so much so that Level 4 AVs aren’t legal in every state, while Level 2 AVs are.
Mohamed Abdel-Aty, an author on the paper and director of the Smart & Safe Transportation Lab at the University of Central Florida, said that while the paper touches on both levels of autonomy, the focus was on Level 4 autonomy. “The model which is the main contribution to this research compared only level 4 to human-driven vehicles,” he said.
And while many findings were generally positive, the authors highlighted two significant negative outcomes for level 4 AVs. It found they were over five times more likely to be involved in an accident at dawn and dusk. They were relatively bad at navigating turns as well, with the odds of an accident during a turn almost doubled compared to those for human-driven vehicles.
The study’s finding of higher accident rates during turns and in unusual lighting conditions highlight two major categories of challenges facing self-driving vehicles: intelligence and data.
J. Christian Gerdes, codirector of the Center for Automotive Research at Stanford University, said turning through traffic is among the most demanding situations for an AV’s artificial intelligence. “That decision is based a lot on the actions of other road users around you, and you’re going to make the choice based on what you predict.”
Cummings agreed with Gerdes. “Any time uncertainty increases [for an AV], you’re going to see an increased risk of accident. Just by the fact you’re turning, that increases uncertainty, and increases risk.”
AVs’ dramatically higher risk of accidents at dawn and dusk, on the other hand, points towards issues with the data captured by a vehicle’s sensors. Most AVs use a combination of radar and visual sensor systems, and the latter is prone to error in difficult lighting.
It’s not all bad news for sensors, though. Level 4 AVs were drastically better in rain and fog, which suggests that the presence of radar and lidar systems gives AVs an advantage in weather conditions that reduce visibility. Gerdes also said AVs, unlike humans, don’t tire or become distracted when driving through weather that requires more vigilance.
While the paper found AVs have a lower risk of accident overall, that doesn’t mean they’ve passed the checkered flag. Gerdes said poor performance in specific scenarios is meaningful and should rightfully make human passengers uncomfortable.
“It’s hard to make the argument that [AVs] are so much safer driving straight, but if [they] get into other situations, they don’t do as well. People will not find that reassuring,” said Gerdes.
The relative lack of data for Level 4 systems is another barrier. Level 4 AVs make up a tiny fraction of all vehicles on the road and only operate in specific areas. AVs are also packed with sensors and driven by an AI system that may make decisions for a variety of reasons that remain opaque in accident data.
While the paper accounts for the low total number of accidents in its statistical analysis, the authors acknowledge more data is necessary to determine the precise cause of accidents, and hope their findings will encourage others to assist. “I believe one of the benefits of this study is to draw the attention of authorities to the need for better data,” said Ding.
On that, Cummings agreed. “We do not have enough information to make sweeping statements,” she said.
A technical paper titled “Chiplets on Wheels: Review Paper on Holistic Chiplet Solutions for Autonomous Vehicles” was published by researchers at the Indian Institute of Technology, Madras.
Abstract
“On the advent of the slow death of Moore’s law, the silicon industry is moving towards a new era of chiplets. The automotive industry is experiencing a profound transformation towards software-defined vehicles, fueled by the surging demand for automotive compute chips, expected to reach 20-22 billion by 2030. High-performance compute (HPC) chips become instrumental in meeting the soaring demand for computational power. Various strategies, including centralized electrical and electronic architecture and the innovative Chiplet Systems, are under exploration. The latter, breaking down System-on-Chips (SoCs) into functional units, offers unparalleled customization and integration possibilities. The research accentuates the crucial open Chiplet ecosystem, fostering collaboration and enhancing supply chain resilience. In this paper, we address the unique challenges that arise when attempting to leverage chiplet-based architecture to design a holistic silicon solution for the automotive industry. We propose a throughput-oriented micro-architecture for ADAS and infotainment systems alongside a novel methodology to evaluate chiplet architectures. Further, we develop in-house simulation tools leveraging the gem5 framework to simulate latency and throughput. Finally, we perform an extensive design of thermally-aware chiplet placement and develop a micro-fluids-based cooling design.”
Find the technical paper here. Published May 2024.
Narashiman, Swathi, Divyaratna Joshi, Deepak Sridhar, Harish Rajesh, Sanjay Sattva, and Varun Manjunath. “Chiplets on Wheels: Review Paper on Holistic Chiplet Solutions for Autonomous Vehicles.” arXiv preprint arXiv:2406.00182 (2024).
The post Adoption of Chiplet Technology in the Automotive Industry appeared first on Semiconductor Engineering.
While christening a new UCLA technology and research center in January, Gov. Gavin Newsom let loose with some fairly typical rhetoric about California's leading-edge role in tech development: "California is the epicenter of global innovation—from the creation of the internet to the dominance of artificial intelligence, humanity's future happens here first."
Yet for the so-called epicenter of innovation, our state certainly doesn't give innovators a lot of room to experiment with new ideas. California lawmakers and regulators are so intent on limiting and controlling any promising new development that we've instead become the poster child for Ronald Reagan's famous quotation: "If it moves, tax it. If it keeps moving, regulate it. And if it stops moving, subsidize it."
Maybe Newsom and the Democratic Legislature haven't noticed, but California has been facing a tech exodus, as many prominent firms leave for states that give them more elbow room to create the next wave of promising innovations. Given the state's dependence on capital gains revenue, it's one reason we're now facing a $45-billion or more budget deficit.
On the good news front, Crunchbase reports that the San Francisco Bay Area may be experiencing a tech resurgence based around artificial intelligence systems, with the region receiving "more than 50 percent of all global venture funding for AI-related startups." But will the state kill that boom before it takes off? Based on the latest actions of the legislature, the answer is "probably."
The Senate Appropriations Committee recently gave the go-ahead to Senate Bill 915, which would "prioritize local control in the decision to deploy autonomous vehicle services." In addition to gaining all the many state approvals, robo-taxi firms would also have to deal with exploding local regulations.
The legislation has been amended to apply to the 15 largest cities and it would forbid localities from banning self-driving cars, but that doesn't ameliorate my concern. This technology is rolling out mainly in big cities anyway. It's easy to kill a technology without outright banning it by, say, forcing these companies to face dramatically different driving rules in every different city where they go.
Like all cutting-edge innovations, self-driving cars strike many of us as an ominous and dangerous development. But most new cars already have various self-driving features (lane assist, adaptive cruise control, blind-spot monitoring). And computers are almost certainly better drivers than people. Nearly 43,000 Americans die in car crashes each year, almost all of them at the hands of human drivers. Widespread A.V. use could save thousands of lives, per research from RAND.
AVs offer fabulous benefits for disabled people, the elderly, and others who cannot or choose not to drive. Yet federal, state, and local officials are worried about a few minor and inevitable problems that have popped up as this technology experiences growing pains—e.g., minor accidents and concerns about traffic violations (as if ordinary drivers don't also sometimes violate traffic laws).
One advocate for S.B. 915 expressed concern about robo-taxis getting stuck at a tricky turn—as if that's a good excuse to add a pointless mish-mash of local regulations to the mix. Ironically, AV development is one area where state regulators have taken an admirably low-key approach. In March, the California Public Utilities Commission gave Waymo, the Alphabet company's driverless-car division, the ability to expand operations in the Bay Area and Los Angeles region and even drive on freeways up to 65 mph. But even when the state takes a sensible approach, the locals want to step in to gum up the works.
And SB 915 isn't the only example of the California Legislature's kneejerk hostility to innovation. Many states are trying to regulate artificial intelligence technology, but California's Senate Bill 1047, which passed out of the Senate and has moved to the Assembly, is easily the most far-reaching example. The bill would create a new state regulatory division to regulate A.I. We all know how effective the state's bureaucrats are at handling complex matters—as well as the impact of lawsuit-promoting statutes.
Basically, the measure forces A.I. developers to mitigate every conceivable harm from their technology by engaging "in speculative fiction about imagined threats of machines run amok, computer models spun out of control, and other nightmare scenarios for which there is no basis in reality," opined an opposition letter from the pro-tech Chamber of Progress. The group rightly fears that the measure undermines California's leading-edge role in the tech sector.
Last week, I wrote about the legislature's effort to limit A.I. technology in a simple, real-world application—self-checkout lanes. Under the guise of helping stores battle retail theft, Senate Bill 1446 is a union concoction designed to limit the use of this technology to protect union grocery jobs.
So, yes, California has been the epicenter of global innovation, but it's apparently not going to continue being so for long. Let's hope Newsom heeds his own words and gets out the veto pen.
This column was first published in The Orange County Register.
The post California's Regulations Might Steer Self-Driving Innovations to Other States appeared first on Reason.com.
Experts at the Table: The automotive ecosystem is undergoing a transformation toward software-defined vehicles, spurring new architectures with more software. Semiconductor Engineering sat down to discuss the impact of these changes with Suraj Gajendra, vice president of products and solutions in Arm‘s automotive line of business; Chuck Alpert, R&D automotive fellow at Cadence; Steve Spadoni, zone controller and power distribution application manager at Infineon; Rebeca Delgado, chief technology officer and principal AI engineer at Intel Automotive; Cyril Clocher, senior director in the automotive product line for high-performance computing at Renesas; David Fritz, vice president, hybrid and virtual systems at Siemens EDA; and Marc Serughetti, senior director, systems design group at Synopsys. What follows are excerpts of that discussion.
L-R: Arm’s Gajendra, Cadence’s Alpert, Infineon’s Spadoni, Intel’s Delgado, Renesas’ Clocher, Siemens’ Fritz, Synopsys’ Serughetti.
SE: The automotive ecosystem is undergoing a technology evolution the likes of which has not been seen, including the move to software-defined vehicles. To set a baseline for this discussion, what is your definition of an SDV?
Gajendra: A software-defined vehicle is a concept, a trend, an idea, where the whole ecosystem can drive new capabilities and new user experiences into the car, even after it rolls out of the showroom or dealership. It’s a pretty loaded concept. There’s a lot of infrastructure that needs to come together, such as software development in the cloud, seamless deployment of that software development onto the car, the whole deployment of over-the-air updates, and the connectivity. In short, the concept of a software-defined vehicle is expecting a world where we can drive new experiences, new capabilities, and new features into the car throughout its lifetime.
Alpert: In thinking about what SDV means, one example is the battery — especially in an EV. I’m not talking about the technology of the battery that’s evolved, but rather the idea that in the past when you wanted to charge your car in your garage and you were worried about starting a fire, you’d think, ‘No, don’t do that because your whole house could burn down.’ The idea is that in the past, maybe we might put a temperature sensor on the battery, but now we actually have software that can monitor it. It might even have AI to predict if the battery is reaching some state that might cause a fire in the future. You also might have something that connects to the power grid and learns when is a good time to charge, because it’s a low-usage period so it’s cheaper. This is just one part of the car, but you can imagine a whole bunch of software that you want to put on top of it in order to connect to the universe. You need a software-defined vehicle platform in order for this, or in all the other parts of your car, to communicate with the world and provide the best user experience.
Spadoni: Infineon’s definition of a software-defined vehicle is a redefining of architecture — specifically, electrical and electronic architecture, feature allocation, and the entire topology of the vehicle, from power generation and storage to power distribution and high compute. It really means new electrical architectures, and it has consequences for the business model of every OEM and Tier 1 involved. It’s a major change to previous methodologies in the last 30 years.
Delgado: Software-defined vehicle is not just over-the-air updates. It’s truly a new methodology and a new philosophy for how to architect every ingredient of the vehicle to continue to deliver value over time, in which the value is very tightly attached to the software that delivers the user experience. Ultimately, this architecture must enable the different practices on how to deliver this new value over time. What’s very interesting is that these practices of moving to software-defined architecture has been done by many other industries already. Intel has a ton of heritage, and actually helped those industries transform. That transformation is truly what we’re observing here. It’s an incredible opportunity, and possibly a crisis if not done right.
Clocher: To apply an analogy here, the car is the new smartphone. But for us, it’s more than that. I’ve heard about the platform, yes, and it’s the major architecture evolution that we’ll see in the next decade. For us at Renesas, it will be a journey that will take time to enhance the user experience, to generate new revenue streams for the industry as it moves from decentralized to centralized classic compute with zonal architecture. We can apply all those buzzwords to a software-defined vehicle. Those platform will need big computers and heavy complex hardware solutions and this will generate evolutions, upgrades to the car during its entire lifetime, but underneath we know — at least at Renesas, and certainly at some other players and silicon vendors — that this will need a huge amount of hardware resources to manage what we have in mind to deploy this platform.
Fritz: I see software-defined vehicles a bit differently than what’s been mentioned so far. For many years, you’d have the hardware team doing their design, and the software team doing their design, and it all needs to come together. There’s an English natural language discussion about what needs to happen, and as we all know, that never really goes terribly well. In automotive that becomes an integration storm, and it is a nightmare. With the new compute requirements that have been mentioned already, that just compounds the issue. So the way I see this is that we tend, as people who have an engineering background, to dive into how we’re going to do things. We hear ‘software-defined vehicle,’ we immediately think about how to do that. There’s not a lot of thought about why it needs to be done, and what needs to happen. We jump into the ‘how’ too early, and a lot of the discussion here is exemplary of that kind of approach. When I’m looking at software-defined vehicles, I’m looking at why it’s important that the software needs to run effectively on a piece of hardware. And for that hardware, why is it important for it to actually operate properly on the software? Then you can decide how to put together a new methodology that’s going to bring those things together. In the past, it’s been called hardware/software co-design. There have been attempts many times, and as has been mentioned, other industries have made this transition. What’s unique about automotive is that it’s not just one transition that needs to happen. It’s hundreds or thousands of transitions. The ecosystem needs to be turned upside down, which we’re seeing happen right now, and you need to bring all that together. It really is a methodology where you need the tooling, you need the processes, you need the thinking, you need the organizations to change so that they can make this transition in a realistic way. SDV is a huge transition. It is a way for the automotive industry to morph into something that has longevity and can meet customer expectations, which it really hasn’t met for some time now.
Serughetti: At the end of the day, if we look starting at the top from our perspective, SDV is a means to bring and enhance the car experience for the customer. That’s the end result that the OEMs look at, but they look at it from the perspective of how that improves the OEM efficiencies, and how that creates new business opportunities. The way we look at it, and what’s important, is the impact it has on the industry, the impact on the processes, on the methodologies, on the people, on the ecosystem, on the technology. It’s really a transformation of the automotive market that is going to fundamentally change how the industry moves forward and bring the OEM into a world in which they are really looking at how they become efficient in delivering cars, how they bring new features, but at the same time, how they evolve their business as well.
SE: As you’ve all described, SDV requires many inter-dependencies, and the entire ecosystem has to have an understanding of the ‘why,’ which should then lead back to laying out the plan for how to get there. Where does the ecosystem stand today in terms of realizing SDV?
Fritz: OEMs have decided in the last few years that they’ve got to take control of their own destiny. They cannot simply take what the suppliers provide. They need a methodology — like this whole SDV concept, and any tooling necessary to provide that — to push down into their suppliers, such that, ‘Here’s what I need. If you can’t do this for me, I will go find someone that will.’ This is not the old ecosystem that bubbled up from the IP to the Tier 2s, to the Tier 1s, and then to the OEMs, which gave them limited choices to go from. So when I say, “Turn the ecosystem upside down,” that’s what is happening. But every OEM has their own ecosystem, and they’re not all in the same place. Even region-to-region, they can be very different.
Delgado: This is a critical discussion, and effectively where the industry has to eventually settle. The magnitude of the transformation of the ecosystem includes roles in the technology evolution. The silicon content is expected to quadruple over the next few years in the vehicle for defining the in-cabin experience of the end user. At the end of the day, the complexity of the transition of roles is of such magnitude that the proprietary, fragmented, and broken approaches that David articulated are really not going to enable the industry to transform at the speed it requires to deliver and meet the experiences. But more than anything, they are not going to address the actual technology changes necessary to implement and allow for this value delivery mechanism. At the end of the day, this is where Intel really believes collaboration is key, and anybody who wants to participate in this ecosystem must provide scalability — also known as top-to-bottom support of the different product lines that our OEMs and Tier 1s are having to support, versus a broken-up approach on these ever-evolving higher performance and higher performance compute needs. It has to be future-proof, because you’re going to launch the vehicle eventually. So certain hardware has to be future-proofed to a certain affordability envelope, and there has to be a strategy around that. And then the ecosystem and that collaboration must be able to deliver that aggregation. It has to be done with certain anchoring technology that will allow us to deliver that performance. Collaboration is key in the sense that these technologies cannot be single-handedly owned, developed, let alone owned, defined, developed, and integrated by OEMs in silos with a proprietary end-to-end architecture definition. There obviously will be differentiations on the actual implementation, but the technologies at large have to have a sense of reuse, particularly from other verticals that have already done software-defined transformations and then tuned in the right ways toward the automotive requirements.
Spadoni: There are probably a wide variety of implementations. At Infineon, we partner with OEMs and Tier 1s and we see different approaches. For example, General Motors has more of a modular approach that emulates what happened in in the mobile phone space. It seems that Ford has a more pragmatic approach, along with Stellantis, but all of them are facing very similar challenges in that affordability has become a big problem. There are multiple generations of implementations that are going to occur, and you’ll see a striving toward how to pay for this extra hardware. It leads to tradeoffs in implementations of other systems that have to have savings in order for them to afford these vehicles. No one ever goes into a dealership and says, ‘Give me a software-defined vehicle.’ Everyone’s looking for value, and you can see it now with volumes going down. There’s a saturation of people buying at the high level. The OEMs want to get more sales, which means they’ll have to go to the lower-cost-value vehicles, and that’s going to affect the electrical and electronic architectures and the software-defined vehicle.
Clocher: What we’re seeing I would summarize as the impact on the ecosystem. We’re moving to an OEM-centric ecosystem. One size does not fit all, meaning OEMs will have their different tastes, their different definitions of levels of integration they want to have in their software-defined vehicle — especially given more complex tasks that we all have to do, rather than the challenge we have to solve, because we’re not talking about a common umbrella of software-defined vehicle. But it really does mean different implementations and different meanings for OEM A from OEM B. I would fully agree with David and Steve that we are far from having a common understanding of, at least, the market itself. And that’s fine, because this will bring differentiation, and ultimately that’s why a customer will go to Dealership A versus Dealership B. This is what the industry wants to see — continue to differentiate, continue to add value to the ultimate product, which is the car.
Serughetti: The important point in all this is, of course, you’re breaking the model that exists today. That’s one of the big challenges. We used to have Tier 1s that were building boxes, and delivering software. This was a complete black box. When it would go to integration, there were all sorts of problems. And now you’re going to break this? The challenge for the OEM is how they do this. They want to control software, but are they equipped to do this today? We see the problems today that some of the legacy OEMs have in setting up their software organizations, the challenges of CARIAD and all such organizations that are trying to do this. It’s not easy to change those companies. Of course, the new entrants don’t have this problem because they are coming from a brand new design versus the ones that deal with legacy. So for the OEM, it’s about how to take control of the software. What does that mean in terms of the processes, in terms of agile development, digital twins, and all of these technologies everybody’s talking about? The other side is, ‘It’s all nice, this software,’ but this software runs on all the companies that are delivering hardware, and that becomes essential to it. You can have the best software, but if your hardware is not there to support performance, power, and all of those aspects, you’re not going to be successful. So the ecosystem is evolving how hardware, software, and all of this comes together. The OEM wants to be the central point. That’s what we’re talking about in terms of the process methodology aspects that are making this transition evolve.
Gajendra: Where are we in this journey? How far have we come? And where are we going? Going back to the point that David mentioned earlier about supply chain evolving and the supply chain turned upside down, five years ago, if we sat here in this sort of a panel and discussed software-defined vehicles, the conversation would have been entirely different. It would have been stuck with the traditional supply chain that we’ve seen for the last 35 or 40 years in the automotive industry. There are fundamentally two aspects here. The supply chain is evolving, and the infrastructure that we, as a community — this team, for example, and many others in the community — are trying to enable is going to be key to making our EDA partners happy. The use of virtual platforms today in the cloud to try and shift left and develop and validate some of these technologies and software wasn’t even there five years ago, so we’ve come a long way. We’ve made a lot of progress together as an industry. Yes, we have a long way to go until we actually have a truly software-defined vehicle. We can go and ask for a software-defined vehicle in the dealership. But the changes we are seeing in terms of all sorts of technology providers trying to make sure that the technology that we eventually will have in the hardware is provided in some sort of virtual form, be it fast models or whatever it is in the cloud, for the vast majority of software ecosystem in automotive this is a big change. I was at Embedded World, and the amount of virtual platforms and the demos that people were actually showing — silicon partners like we have here, Intel, Renesas, Infineon, EDA companies — pointed to a strong movement of, ‘Let’s build the infrastructure that we can build, and then provide that infrastructure to the OEMs to take it from there.’ There is a lot of work going on. Together we will make the infrastructure across the board, be it virtual platform or others, richer and more capable.
Alpert: For sure, OEMs have to control their own destiny. In the past, they would do it by differentiating maybe because they had better engine performance, or some other feature. But going forward, the differentiation is going to be their software. Whoever can make software that will provide additional value, and brand it, that’s going to be the differentiator and that’s the trend. In terms of how you get there, a shared ecosystem is important. SOAFEE is a potential way that, together with virtual platforms, you can provide a shared ecosystem for development, but still allow everyone to differentiate and plug-and-play. That’s one reason we’re working closely with Arm on trying to have a reference design specifically for this purpose. But again, we’re not saying, ‘This is the design you use. This is how you do it.’ That’s not it. The point is, let’s start somewhere, and then people can start swapping out pieces and doing different things. As long as OEMs can plug-and-play, then they can still differentiate. But they don’t have to invent everything themselves, which would be too costly.
Related Reading
Software-Defined Vehicles Ready To Roll
New approach could have big effects on cost, safety, security, and time to market.
The post Software-Defined Vehicle Momentum Grows appeared first on Semiconductor Engineering.
Experts at the Table: The automotive ecosystem is undergoing a transformation toward software-defined vehicles, spurring new architectures with more software. Semiconductor Engineering sat down to discuss the impact of these changes with Suraj Gajendra, vice president of products and solutions in Arm‘s automotive line of business; Chuck Alpert, R&D automotive fellow at Cadence; Steve Spadoni, zone controller and power distribution application manager at Infineon; Rebeca Delgado, chief technology officer and principal AI engineer at Intel Automotive; Cyril Clocher, senior director in the automotive product line for high-performance computing at Renesas; David Fritz, vice president, hybrid and virtual systems at Siemens EDA; and Marc Serughetti, senior director, systems design group at Synopsys. What follows are excerpts of that discussion.
L-R: Arm’s Gajendra, Cadence’s Alpert, Infineon’s Spadoni, Intel’s Delgado, Renesas’ Clocher, Siemens’ Fritz, Synopsys’ Serughetti.
SE: The automotive ecosystem is undergoing a technology evolution the likes of which has not been seen, including the move to software-defined vehicles. To set a baseline for this discussion, what is your definition of an SDV?
Gajendra: A software-defined vehicle is a concept, a trend, an idea, where the whole ecosystem can drive new capabilities and new user experiences into the car, even after it rolls out of the showroom or dealership. It’s a pretty loaded concept. There’s a lot of infrastructure that needs to come together, such as software development in the cloud, seamless deployment of that software development onto the car, the whole deployment of over-the-air updates, and the connectivity. In short, the concept of a software-defined vehicle is expecting a world where we can drive new experiences, new capabilities, and new features into the car throughout its lifetime.
Alpert: In thinking about what SDV means, one example is the battery — especially in an EV. I’m not talking about the technology of the battery that’s evolved, but rather the idea that in the past when you wanted to charge your car in your garage and you were worried about starting a fire, you’d think, ‘No, don’t do that because your whole house could burn down.’ The idea is that in the past, maybe we might put a temperature sensor on the battery, but now we actually have software that can monitor it. It might even have AI to predict if the battery is reaching some state that might cause a fire in the future. You also might have something that connects to the power grid and learns when is a good time to charge, because it’s a low-usage period so it’s cheaper. This is just one part of the car, but you can imagine a whole bunch of software that you want to put on top of it in order to connect to the universe. You need a software-defined vehicle platform in order for this, or in all the other parts of your car, to communicate with the world and provide the best user experience.
Spadoni: Infineon’s definition of a software-defined vehicle is a redefining of architecture — specifically, electrical and electronic architecture, feature allocation, and the entire topology of the vehicle, from power generation and storage to power distribution and high compute. It really means new electrical architectures, and it has consequences for the business model of every OEM and Tier 1 involved. It’s a major change to previous methodologies in the last 30 years.
Delgado: Software-defined vehicle is not just over-the-air updates. It’s truly a new methodology and a new philosophy for how to architect every ingredient of the vehicle to continue to deliver value over time, in which the value is very tightly attached to the software that delivers the user experience. Ultimately, this architecture must enable the different practices on how to deliver this new value over time. What’s very interesting is that these practices of moving to software-defined architecture has been done by many other industries already. Intel has a ton of heritage, and actually helped those industries transform. That transformation is truly what we’re observing here. It’s an incredible opportunity, and possibly a crisis if not done right.
Clocher: To apply an analogy here, the car is the new smartphone. But for us, it’s more than that. I’ve heard about the platform, yes, and it’s the major architecture evolution that we’ll see in the next decade. For us at Renesas, it will be a journey that will take time to enhance the user experience, to generate new revenue streams for the industry as it moves from decentralized to centralized classic compute with zonal architecture. We can apply all those buzzwords to a software-defined vehicle. Those platform will need big computers and heavy complex hardware solutions and this will generate evolutions, upgrades to the car during its entire lifetime, but underneath we know — at least at Renesas, and certainly at some other players and silicon vendors — that this will need a huge amount of hardware resources to manage what we have in mind to deploy this platform.
Fritz: I see software-defined vehicles a bit differently than what’s been mentioned so far. For many years, you’d have the hardware team doing their design, and the software team doing their design, and it all needs to come together. There’s an English natural language discussion about what needs to happen, and as we all know, that never really goes terribly well. In automotive that becomes an integration storm, and it is a nightmare. With the new compute requirements that have been mentioned already, that just compounds the issue. So the way I see this is that we tend, as people who have an engineering background, to dive into how we’re going to do things. We hear ‘software-defined vehicle,’ we immediately think about how to do that. There’s not a lot of thought about why it needs to be done, and what needs to happen. We jump into the ‘how’ too early, and a lot of the discussion here is exemplary of that kind of approach. When I’m looking at software-defined vehicles, I’m looking at why it’s important that the software needs to run effectively on a piece of hardware. And for that hardware, why is it important for it to actually operate properly on the software? Then you can decide how to put together a new methodology that’s going to bring those things together. In the past, it’s been called hardware/software co-design. There have been attempts many times, and as has been mentioned, other industries have made this transition. What’s unique about automotive is that it’s not just one transition that needs to happen. It’s hundreds or thousands of transitions. The ecosystem needs to be turned upside down, which we’re seeing happen right now, and you need to bring all that together. It really is a methodology where you need the tooling, you need the processes, you need the thinking, you need the organizations to change so that they can make this transition in a realistic way. SDV is a huge transition. It is a way for the automotive industry to morph into something that has longevity and can meet customer expectations, which it really hasn’t met for some time now.
Serughetti: At the end of the day, if we look starting at the top from our perspective, SDV is a means to bring and enhance the car experience for the customer. That’s the end result that the OEMs look at, but they look at it from the perspective of how that improves the OEM efficiencies, and how that creates new business opportunities. The way we look at it, and what’s important, is the impact it has on the industry, the impact on the processes, on the methodologies, on the people, on the ecosystem, on the technology. It’s really a transformation of the automotive market that is going to fundamentally change how the industry moves forward and bring the OEM into a world in which they are really looking at how they become efficient in delivering cars, how they bring new features, but at the same time, how they evolve their business as well.
SE: As you’ve all described, SDV requires many inter-dependencies, and the entire ecosystem has to have an understanding of the ‘why,’ which should then lead back to laying out the plan for how to get there. Where does the ecosystem stand today in terms of realizing SDV?
Fritz: OEMs have decided in the last few years that they’ve got to take control of their own destiny. They cannot simply take what the suppliers provide. They need a methodology — like this whole SDV concept, and any tooling necessary to provide that — to push down into their suppliers, such that, ‘Here’s what I need. If you can’t do this for me, I will go find someone that will.’ This is not the old ecosystem that bubbled up from the IP to the Tier 2s, to the Tier 1s, and then to the OEMs, which gave them limited choices to go from. So when I say, “Turn the ecosystem upside down,” that’s what is happening. But every OEM has their own ecosystem, and they’re not all in the same place. Even region-to-region, they can be very different.
Delgado: This is a critical discussion, and effectively where the industry has to eventually settle. The magnitude of the transformation of the ecosystem includes roles in the technology evolution. The silicon content is expected to quadruple over the next few years in the vehicle for defining the in-cabin experience of the end user. At the end of the day, the complexity of the transition of roles is of such magnitude that the proprietary, fragmented, and broken approaches that David articulated are really not going to enable the industry to transform at the speed it requires to deliver and meet the experiences. But more than anything, they are not going to address the actual technology changes necessary to implement and allow for this value delivery mechanism. At the end of the day, this is where Intel really believes collaboration is key, and anybody who wants to participate in this ecosystem must provide scalability — also known as top-to-bottom support of the different product lines that our OEMs and Tier 1s are having to support, versus a broken-up approach on these ever-evolving higher performance and higher performance compute needs. It has to be future-proof, because you’re going to launch the vehicle eventually. So certain hardware has to be future-proofed to a certain affordability envelope, and there has to be a strategy around that. And then the ecosystem and that collaboration must be able to deliver that aggregation. It has to be done with certain anchoring technology that will allow us to deliver that performance. Collaboration is key in the sense that these technologies cannot be single-handedly owned, developed, let alone owned, defined, developed, and integrated by OEMs in silos with a proprietary end-to-end architecture definition. There obviously will be differentiations on the actual implementation, but the technologies at large have to have a sense of reuse, particularly from other verticals that have already done software-defined transformations and then tuned in the right ways toward the automotive requirements.
Spadoni: There are probably a wide variety of implementations. At Infineon, we partner with OEMs and Tier 1s and we see different approaches. For example, General Motors has more of a modular approach that emulates what happened in in the mobile phone space. It seems that Ford has a more pragmatic approach, along with Stellantis, but all of them are facing very similar challenges in that affordability has become a big problem. There are multiple generations of implementations that are going to occur, and you’ll see a striving toward how to pay for this extra hardware. It leads to tradeoffs in implementations of other systems that have to have savings in order for them to afford these vehicles. No one ever goes into a dealership and says, ‘Give me a software-defined vehicle.’ Everyone’s looking for value, and you can see it now with volumes going down. There’s a saturation of people buying at the high level. The OEMs want to get more sales, which means they’ll have to go to the lower-cost-value vehicles, and that’s going to affect the electrical and electronic architectures and the software-defined vehicle.
Clocher: What we’re seeing I would summarize as the impact on the ecosystem. We’re moving to an OEM-centric ecosystem. One size does not fit all, meaning OEMs will have their different tastes, their different definitions of levels of integration they want to have in their software-defined vehicle — especially given more complex tasks that we all have to do, rather than the challenge we have to solve, because we’re not talking about a common umbrella of software-defined vehicle. But it really does mean different implementations and different meanings for OEM A from OEM B. I would fully agree with David and Steve that we are far from having a common understanding of, at least, the market itself. And that’s fine, because this will bring differentiation, and ultimately that’s why a customer will go to Dealership A versus Dealership B. This is what the industry wants to see — continue to differentiate, continue to add value to the ultimate product, which is the car.
Serughetti: The important point in all this is, of course, you’re breaking the model that exists today. That’s one of the big challenges. We used to have Tier 1s that were building boxes, and delivering software. This was a complete black box. When it would go to integration, there were all sorts of problems. And now you’re going to break this? The challenge for the OEM is how they do this. They want to control software, but are they equipped to do this today? We see the problems today that some of the legacy OEMs have in setting up their software organizations, the challenges of CARIAD and all such organizations that are trying to do this. It’s not easy to change those companies. Of course, the new entrants don’t have this problem because they are coming from a brand new design versus the ones that deal with legacy. So for the OEM, it’s about how to take control of the software. What does that mean in terms of the processes, in terms of agile development, digital twins, and all of these technologies everybody’s talking about? The other side is, ‘It’s all nice, this software,’ but this software runs on all the companies that are delivering hardware, and that becomes essential to it. You can have the best software, but if your hardware is not there to support performance, power, and all of those aspects, you’re not going to be successful. So the ecosystem is evolving how hardware, software, and all of this comes together. The OEM wants to be the central point. That’s what we’re talking about in terms of the process methodology aspects that are making this transition evolve.
Gajendra: Where are we in this journey? How far have we come? And where are we going? Going back to the point that David mentioned earlier about supply chain evolving and the supply chain turned upside down, five years ago, if we sat here in this sort of a panel and discussed software-defined vehicles, the conversation would have been entirely different. It would have been stuck with the traditional supply chain that we’ve seen for the last 35 or 40 years in the automotive industry. There are fundamentally two aspects here. The supply chain is evolving, and the infrastructure that we, as a community — this team, for example, and many others in the community — are trying to enable is going to be key to making our EDA partners happy. The use of virtual platforms today in the cloud to try and shift left and develop and validate some of these technologies and software wasn’t even there five years ago, so we’ve come a long way. We’ve made a lot of progress together as an industry. Yes, we have a long way to go until we actually have a truly software-defined vehicle. We can go and ask for a software-defined vehicle in the dealership. But the changes we are seeing in terms of all sorts of technology providers trying to make sure that the technology that we eventually will have in the hardware is provided in some sort of virtual form, be it fast models or whatever it is in the cloud, for the vast majority of software ecosystem in automotive this is a big change. I was at Embedded World, and the amount of virtual platforms and the demos that people were actually showing — silicon partners like we have here, Intel, Renesas, Infineon, EDA companies — pointed to a strong movement of, ‘Let’s build the infrastructure that we can build, and then provide that infrastructure to the OEMs to take it from there.’ There is a lot of work going on. Together we will make the infrastructure across the board, be it virtual platform or others, richer and more capable.
Alpert: For sure, OEMs have to control their own destiny. In the past, they would do it by differentiating maybe because they had better engine performance, or some other feature. But going forward, the differentiation is going to be their software. Whoever can make software that will provide additional value, and brand it, that’s going to be the differentiator and that’s the trend. In terms of how you get there, a shared ecosystem is important. SOAFEE is a potential way that, together with virtual platforms, you can provide a shared ecosystem for development, but still allow everyone to differentiate and plug-and-play. That’s one reason we’re working closely with Arm on trying to have a reference design specifically for this purpose. But again, we’re not saying, ‘This is the design you use. This is how you do it.’ That’s not it. The point is, let’s start somewhere, and then people can start swapping out pieces and doing different things. As long as OEMs can plug-and-play, then they can still differentiate. But they don’t have to invent everything themselves, which would be too costly.
Related Reading
Software-Defined Vehicles Ready To Roll
New approach could have big effects on cost, safety, security, and time to market.
The post Software-Defined Vehicle Momentum Grows appeared first on Semiconductor Engineering.
Last year Mozilla released a report showcasing how the auto industry has some of the worst privacy practices of any tech industry in America (no small feat). Massive amounts of driver behavior is collected by your car, and even more is hoovered up from your smartphone every time you connect. This data isn’t secured, often isn’t encrypted, and is sold to a long list of dodgy, unregulated middlemen.
Last March the New York Times revealed that automakers like GM routinely sell access to driver behavior data to insurance companies, which then use that data to justify jacking up your rates. The practice isn’t clearly disclosed to consumers, and has resulted in 11 federal lawsuits in less than a month.
Now Ron Wyden’s office is back with the results of their preliminary investigation into the auto industry, finding that it routinely provides customer data to law enforcement without a warrant without informing consumers. The auto industry, unsurprisingly, couldn’t even be bothered to adhere to a performative, voluntary pledge the whole sector made in 2014 to not do precisely this sort of thing:
“Automakers have not only kept consumers in the dark regarding their actual practices, but multiple companies misled consumers for over a decade by failing to honor the industry’s own voluntary privacy principles. To that end, we urge the FTC to investigate these auto manufacturers’ deceptive claims as well as their harmful data retention practices.”
The auto industry can get away with this because the U.S. remains too corrupt to pass even a baseline privacy law for the internet era. The FTC, which has been left under-staffed, under-funded, and boxed in by decades of relentless lobbying and mindless deregulation, lacks the resources to pursue these kinds of violations at any consistent scale; precisely as corporations like it.
Maybe the FTC will act, maybe it won’t. If it does, it will take two years to get the case together, the financial penalties will be a tiny pittance in relation to the total amount of revenues gleaned from privacy abuses, and the final ruling will be bogged down in another five years of legal wrangling.
This wholesale violation of user privacy has dire, real-world consequences. Wyden’s office has also been taking aim at data brokers who sell abortion clinic visitor location data to right wing activists, who then have turned around to target vulnerable women with health care disinformation. Wireless carrier location data has also been abused by everyone from stalkers to people pretending to be law enforcement.
The cavalier treatment of your auto data poses those same risks, Wyden’s office notes:
“Vehicle location data can reveal intimate details of a person’s life, including for those who seek care across state lines, attend protests, visit mental or behavioral health professionals or seek treatment for substance use disorder.”
Keep in mind this is the same auto industry currently trying to scuttle right to repair reforms under the pretense that they’re just trying to protect consumer privacy (spoiler: they aren’t).
This same story is playing out across a litany of industries. Again, it’s just a matter of time until there’s a privacy scandal so massive and ugly that even our corrupt Congress is shaken from its corrupt apathy, though you’d hate to think what it will have to look like.
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