<h2 dir="ltr" id="isPasted">FREE REPORT:&nbsp;Reducing Human Driver Error and Setting Realistic Expectations with Advanced Driver Assistance Systems</h2>Thousands die or are injured each year in automobile crashes. Bringing automated driving systems technologies into the advanced driver assist systems (ADAS) and connected vehicle space will help humans drive more safely and better prepare us for automated vehicles (AVs).Learn more about ADAS and ADS implementation with the goal of reaching zero deaths and serious injuries by downloading this FREE report from SAE International (valued at $50).&nbsp;<img src="https://images.wevolver.com/eyJidWNrZXQiOiJ3ZXZvbHZlci1wcm9qZWN0LWltYWdlcyIsImtleSI6ImZyb2FsYS8xNzAxNjg3NzIwMzE4LXNhZSBmcmVlIHJlcG9ydC5qcGciLCJlZGl0cyI6eyJyZXNpemUiOnsid2lkdGgiOjk1MCwiZml0IjoiY292ZXIifX19" style="width: 300px;" class="fr-fic fr-dib"><h2 dir="ltr"><iframe width="540" height="740" src="https://c8056756.sibforms.com/serve/MUIFAIm7QnvQQ9ZyzkX--H5jNS-TSb9GWr5SQBXi_SUjRPpt67CBveq_yevZYMSeiJ-O1dwYw05f2kl-Q7sfNDRem1Y-03GXYZV4qkTbJeR_K_VpJzgMk_8Ry8fjyfgJ7fAMrCmcRHc1KXsSTuhIU5p5pBa3F1-TGDgOdQgcF-pU8cSTrl8JF2j2b9gl6-5e66Ro-rltcIrGxsRG" frameborder="0" scrolling="auto" allowfullscreen="" style="display: block;margin-left: auto;margin-right: auto;max-width: 100%;" class="fr-draggable" data-lf-form-tracking-inspected-kn9eq4roawkarlvp="true" data-lf-yt-playback-inspected-kn9eq4roawkarlvp="true" data-lf-vimeo-playback-inspected-kn9eq4roawkarlvp="true"></iframe> </h2><h2 dir="ltr">Understanding software-defined vehicles</h2>The phrase “software-defined vehicle” has embedded in the vehicle-development lexicon as the catchall for a new era of digitally driven products. But there is persistent disagreement about even the phrase’s definition, much less the engineering scope required to transition from the industry’s hardware-intensive history to a software-driven environment.Leading into SAE’s WCX 2023 conference earlier this year, SAE Media spoke with Maria Anhalt, CEO at Elektrobit, the 35-year-old developer of software products and services for the auto industry that is an independently operated but wholly owned subsidiary of global supplier Continental.Elektrobit had just announced that Jaguar Land Rover would contract for the company’s software platform and Automotive OS (operating system) to build JLR’s next-generation electrical architecture, and in early August Elektrobit added a collaboration with NXP in which Elektrobit’s software for developing automotive ECUs will support NXP’s S32G3-series vehicle network processors.Anhalt discussed the definition of “software-defined vehicles,” the confusion over what automotive “operating systems” really encompass and why the bulk of automotive software actually is “non-differentiating.”SAE media: There is a lot of maneuvering in this space with operating systems (OSs) and who's really going to be in charge of that. You clearly want to be involved in the development of vehicle OSs, but some automakers say they want to do it themselves. Help us understand this landscape.Ahalt: I'll start with who we are: Elektrobit is a pure-play company ─ systems and software ─ we don't do hardware. We do have hardware engineers, however.The software-defined vehicle starts from the software and the systems side ─ and I'll explain it in a bit. Being a software company, we earn money with commercial software: software products and software services. What's the difference? A software product is something that solves a problem that is a pattern, you buy it again and again. Think of Microsoft Office, something that you purchase a perpetual license, software as a product, software as a service, and multiple customers can use it, this is the product. Obviously, these products are not differentiating for the end user. For the people who are providing it, it's differentiating, but who is buying it, it's not differentiating.A software service is — and this is where the automotive industry is coming from — there is an OEM, there is a car maker who comes and says, ‘Hey, I need A, B, C, D, here's my book of requirements, 400 pages, you write it for me.’ This is a service. You write software for somebody, this is differentiating for them, the IP is with the person or the company who orders it; we write the software. This is the difference between a product and services. We do both.SAE Media: A service then, a customer comes to you essentially with an RFQ to say, ‘Can you do this for us?’Anhalt:&nbsp;Yes, we do products and we do services. This is one of our co-differentiating principles; there are companies thates. There are a lot of companies that do primarily services.When you sit in a modern vehicle, there is a ton of software inside. We have done investigation and studies and interviews with carmakers and we estimate that approximately 60 percent of the software that is in the car is something that you're not seeing — you as an end user. You're not seeing and you're not experiencing it. I had a discussion with McKinsey last year and they were saying that they estimate 75 [percent]. Whether it's 60 percent or 75 percent or 68 percent, it doesn't matter a lot.A lot of software is essential. It's important, but it's not something that the end user, the end customer, is seeing. We refer to this as non-differentiating, meaning it's non-differentiating for the carmaker, it's non-differentiating for the end user. But for us it's differentiating, around 40 percent is differentiating. Think of the software where the ‘trademark’ is visible: entertainment, the cockpit, automated-driving functions, where you sit in a BMW or in a Tesla and you don't even see the logo, but you know it's a different car. These are differentiating aspects.SAE Media: Separating the differentiating from the non-differentiating helps everyone in the value chain, then?Anhalt: If we position our product for the non-differentiating piece, then this is how we help customers run faster, provide a higher quality of service and save money. Because if you do not need to reinvent the wheel again and again and can base your development on software that is well-established, that is used by multiple other players, tested for many other corner cases, it's foundational.Our foundational software is where we excel and we provide products, we also provide the maintenance, the upgrade ability and everything because this is how we help the customer run faster, provide faster innovation, increase the quality of the service and everything. An example of this is the [Jaguar Land Rover collaboration] — we have more customers that are basing their development on our software, but this is one famous customer that is publicly speaking about this non-differentiating [component of vehicle software].Differentiating is where we do something for the user experience so visible that is differentiating, and this is an example for us. You've seen probably seen Afeela, the Sony-Honda mobility [vehicle concept]. This is something for the differentiated piece — our whole piece of this work that we have done for Sony is on the software platform as well, but we also did the end-to-end, we did the system-level design, we did the displays, we did the hardware requirements, we did the software platform, the architecture, the implementation and we did the whole cockpit. Now what you experience in the cockpit, all these displays, L-swipe display, this feeling, the different experience in the car, this is differentiating.SAE Media:&nbsp;Where does an automotive operating system fit into all this?Anhalt: When we say automotive OS — I studied computer science, so the operating system per se is something that you don't see, it's in the vehicle. But when OEMs speak about automotive OS, it's not an OS in the computer-science terminology, this is why it's so confusing.When Volkswagen CARIAD, for example, speaks about automotive OS, they mean the entire vertical stack. You have hardware, then you have some software that is very hardware-specific, then we have the more standard operating system like Linux, the things like this. Then you'll have the hypervisors, then you'll have the middleware, then you have the platform services, then you have the abstraction layer with the software-defined kit, and then you have the functions. When CARIAD says ‘Volkswagen OS,’ they mean the entire vertical stack. When Daimler or Mercedes-Benz talks about the ‘MBOS,’ for them it's also a vertical stack.SAE Media:&nbsp;When an automaker refers to OS, they are talking about something that would be everything from software that governs the body controller or the engine-management system, all the way to the screen in the cabin?Anhalt: Yeah, without the hardware, exactly.When I became the CEO of Elektrobit in January 2021, I was thinking, ‘Shall I use the same terminology?’ I had a torn heart — it's not true if you're a computer scientist, it's very confusing, but if your customers are using that terminology… You cannot implement everything. On one hand, it's just not possible, but what we believe is that there must be a lot of standardization for the non-differentiating pieces that are repeatable that everyone needs, and we must work across the ecosystem to have more and more people reuse these pieces —&nbsp;and differentiate with the functions.SAE Media: Why is that? Is it a time savings? Is it a money savings? Is it both?Anhalt: It’s all of those? Because right now, this is part of the problem with this ‘spaghetti code.’ Of course, there's complexity and of course there's safety and everything. But the OEMs have been doing development, "This is the hardware, this is how it works."With every SOP, with every new product line, they start to develop from scratch every time, and this is why cost is going up; it takes more time, then the quality is low. If we start building software like for the phones — I have an older Samsung phone and I still can upgrade it, it's still an Android, and it's only possible with that, the hardware is decoupled from the software. You can always upgrade it. There is the iPhone, you can upgrade it quite often with a new operating system and the hardware is the same.SAE Media: Part of the disconnect for some people watching the industry is that the automakers have talked for some time about how many software engineers they're hiring. Do you think that's a mistake, that they should let you do that, and then they can go on about the business of developing a car?Anhalt:&nbsp;Yes and no. We believe that a carmaker could do everything on their own, but it is very expensive and very slow, and probably there is one or two – max — carmakers in the world who will have the financial power and endurance to go this way. Carmakers have a lot of innovation, they're great companies, but they're not software companies, and there is not even enough talent. We do not have so many software engineers in the world to do everything.I truly believe that OEMs must acquire software expertise, they must have software engineers in-house. However, team up and leverage the products, the software that already exists, build it in — do not reinvent it again —vv and invest the software capabilities into differentiating features, functions that are relevant for the brand.SAE Media:&nbsp;‘Software-defined’ has quickly become a catchphrase – everyone in almost every corner of the industry uses the term. Where do you begin to really define software-defined?Anhalt:&nbsp;For me, the Sony [Afeela] car is one of the very first true software-defined cars; what Tesla is doing is a software-defined car.SAE Media:&nbsp;You view Teslas, then, as software-defined vehicles?Anhalt:&nbsp;Tesla started as a software company; Tesla is a software company that happens to produce cars. They differentiate with software, not with the hardware. They do EVs, but differentiate with the software. They learn with the software, they update with it, their business model is based on the software.When Sony did the Afeela car, they started with a software, software-defined. We worked together to define a concept for user and experience, everything user-experience from the use case, how you will [interact] with the displays for the cockpit. We know how to do software in the car, Sony knows how to do behavioral studies, how to do user-experience with the gadgets. From there, we define the requirements for the hardware, we define the requirements for the platform, for the architecture — we implement this.<img src="https://images.wevolver.com/eyJidWNrZXQiOiJ3ZXZvbHZlci1wcm9qZWN0LWltYWdlcyIsImtleSI6ImZyb2FsYS8xNzAxNjg2MTM5MzY3LWVsZWt0cm9iaXQtc29mdHdhcmUtZGVmaW5lZC1zb255LWFmZWVsYS1mMzRfZ2FsbGVyeS5qcGciLCJlZGl0cyI6eyJyZXNpemUiOnsid2lkdGgiOjk1MCwiZml0IjoiY292ZXIifX19" style="width: 766px;" class="fr-fic fr-dib">Sony Afeela is intended to help usher a new age of passenger-vehicle development direction. (Sony Afeela)

One kind of software-defined vehicle architecture as represented by AUTOSAR, the global partnership of companies in the automotive and software industries collaborating to develop and establish a standardized software framework and vehicle E/E system open architecture. (AUTOSAR/Elektrobit)

Elektrobit CEO discusses the landscape of automotive software development and explains why a lot of software doesn’t necessarily have to be all that transformational.

Defining the software of 'software-defined' vehicles

RISC-V is inevitable - it became the mantra of RISC-V, and it's true. But before we see why that is, let’s step back and discuss what RISC-V is and why we should care.Back in the early days, computers were large and bulky, taking up entire rooms and making a lot of noise.It's incredible to think about how much technology has advanced over the years. Nowadays, we're surrounded by microprocessors that make our lives easier without us even realizing it. For example, when we use our bank cards to pay at the supermarket, we're actually using a full computer that encrypts, signs and verifies our transactions. And our phones contain dozens and dozens of processors that make all the conveniences of modern life possible.<h2>What is RISC-V?</h2>RISC-V (Reduced Instruction Set Computers) is an open-source Instruction Set Architecture (ISA). You may have heard of other well-known ISAs such as x86, ARM, Power, and MIPS.&nbsp;The RISC-V ISA is the common, standardized language between the processor (hardware) and the programs running on it (software). With RISC-V, the processor itself can only do very basic things, such as (conditionally) adding or subtracting two numbers and deciding what to do based on those arithmetic outcomes. It can also repeat those arithmetic operations several times until a certain condition is reached. In fact, there are only 37 instructions in the base. But these are the very basic ingredients needed to describe essentially any problem to the processor.&nbsp;We call those commands, instructions.&nbsp;By defining the exact meaning (semantic) and spelling (syntax) of those instructions, you can start to build a common understanding between the processor and the software running on it. You can think of the ISA as a dictionary. The ISA of RISC-V is free for anyone to access here. This is incredibly important because today’s software has become so complex that no single company can manage it alone and a common standard ensures interoperability.&nbsp;RISC-V came to the right place, by the right people, at the right time.&nbsp;David Patterson, a visionary in the field of computer architecture, spearheaded the development of Reduced Instruction Set Computers (RISC) at Berkeley. The project went through five iterations, which ultimately led to the creation of the RISC-V architecture.Initially, RISC-V mainly piqued academic curiosity. However, the availability of open-source processor implementations, coupled with rising geopolitical tensions, quickly drew industrial interest as well. From that point, the project experienced a snowball effect gaining momentum and broader adoption.<h2>Open source breeds innovation</h2>The primary advantage of RISC-V lies in its licensing flexibility. Although the specifications for other ISAs are publicly available, legal constraints prevent you from implementing and selling processor hardware unless you have obtained a license for the ISA specification. With RISC-V, this limitation is removed, offering greater freedom for innovation and commercialization.The limits that most ISAs have reduce their use by smaller innovators. For example, the widely used ISA “x86” allows only a handful of licensees. Arm, another well-known provider of processor IP, has an entirely different business model. They are selling processor IPs that can be licensed and integrated into your product. Only the biggest players in the market are able to own an architectural license that allows them to implement their own processors.&nbsp;Most notable of these is Apple, which switched its entire product line from Intel x86 processors to its own Arm implementation. Their investment in this endeavor proved to be a wise decision both technically and economically, as it enabled them to distinguish their product line even further and surpass their rivals by a significant margin.In the past, designing your own processor and utilizing the expansive software ecosystem was thought to be reserved solely for large corporations. However, that's not entirely true. Alternatives like OpenRISC existed, but they weren't widely adopted because their software ecosystems were underdeveloped, and they were often considered hobbyist projects.Another notable benefit of RISC-V is its built-in modularity, which contrasts with ARM's more rigid structure. The RISC-V architecture is designed from the ground up to be modular, allowing for easier customization and innovation. While ARM tightly controls and protects its ISA specification, making it difficult to introduce your own specialized features, RISC-V offers the flexibility to add custom extensions and your “secret sauce.”This is particularly advantageous if you aim to target specific market segments and differentiate your product from competitors. Remember those 37 instructions I’ve mentioned? Turns out this is only the very base instruction set. For most applications, you would likely want to include some more specialized instructions such as floating-point support, atomic memory operations, or maybe even scalable vector extensions. You don’t need to, but you can. In the same way, your company could add your own instructions in case those are beneficial for the application in mind. This means you can still leverage the entire software ecosystem that has been and is being built and only add value on top instead of reinventing the wheel. So let’s have a look at some of these advantages.&nbsp;<h2>How can RISC-V help start-ups design their own chips?</h2>For me, the two most important aspects for a hardware-producing start-up are time-to-market and freedom to innovate.&nbsp;Most silicon/hardware start-ups innovate and try to provide value-add in their niche. The processor is a necessary, boring commodity and usually not the main star of the product. This is good and the way it should be. Otherwise, we would all be selling the same processors.&nbsp;RISC-V is an absolute game changer for that.&nbsp;Do you want to quickly try out an integration idea? Prototype something on an FPGA? Before RISC-V this either meant you needed to use some custom FPGA vendor’s ISA (which you couldn’t send to manufacture for your final product) or start negotiations with the processor vendor of your choice. Negotiations are hard for a small silicon start-up. With RISC-V you can go and grab some core from the internet, do your trials, and start building your software stack. Once happy with the prototype and you want to move forward, there are a plethora of RISC-V IP vendors you can choose from. Choice and market competition are a good thing.Expanding on the points raised earlier, the freedom to innovate with RISC-V comes without the risk of architectural lock-in. This has two advantages, first, you are free to switch IP vendors. Your software stack will still work the same because you adhered to an open standard. Second, you can provide your own extensions on top of the main RISC-V standards, and the company can provide very concentrated value-add without the need to maintain or develop commodity.&nbsp;<h3>Let’s have a look at what we’ve done at Axelera.&nbsp;</h3>At the heart of our Metis AI Processing Unit, there are four AI Cores. Each AI core provides a 512x512 matrix-vector multiplication (MVM) in-memory compute array and a vector datapath that operates on streams of data.&nbsp;To provide a generic solution that can keep up with the fast evolution in the field of neural networks we kept the datapath control as flexible as possible. We aim to push as much as possible into low-level driver software where we can innovate, correct, and adapt throughout the product’s life cycle. Therefore, each AI Core has a dedicated RISC-V, application-class core, that has full control over the datapath unit. A 5th system controller provides SoC management and workload dispatch to the individual AI cores. We’ve confidently chosen the RISC-V ISA because we could start virtual prototyping and building our software stack on open and free software far before we had done any IP negotiations. Furthermore, we knew that the IP landscape would provide us with sufficient competitive choices for our needs.&nbsp;In roughly a year, we went from concept/FPGA prototype to tape-out, and in just a couple of days after having received the chips back, we could run the first end-to-end neural networks on our lab benches. Taking the limited resources and this aggressive time schedule into account it was clear that we’ll needed to buy proven IP without long negotiations to kick-start our developments.&nbsp;Choosing to proceed with RISC-V ensured that we could continue to innovate in future generations and further distinguish our offerings.So, is it all love, peace, and harmony? Not exactly. Like anything that's still a work in progress. RISC-V has its own set of hiccups and curveballs.An ISA is mainly about instructions, but a processor is part of a bigger system that includes memory, peripherals, and other processors. This broader setup, often called a platform, isn't covered in the main ISA spec. The RISC-V Foundation aims to standardize these aspects too.However, details matter. Things like atomic handling in multi-hart systems may only be standardized for specific platforms. Also, some IP providers might have legacy elements because they developed solutions before RISC-V standards were set. Standards are scattered, so you have to keep an eye out. And given the rapid growth, it's tough to stay updated on all the ongoing activities.<h2>Looking ahead</h2>Remember those room-filling mainframes and how technology has continuously shrunk down to fit our pockets and wrists? Just like those evolutionary leaps, RISC-V aims to be a cornerstone in the next phase of computational progress.In an ideal future, RISC-V will be as "boring" as any well-established technology—boring in the sense that it's reliable, stable, and so embedded in our daily lives that we take it for granted. The long-term vision is for software to run seamlessly on RISC-V-based processors without users having to worry about compatibility issues.In the short term, however, there's still much to be done. While RISC-V is showing promise in deeply embedded systems like smartcards and IoT devices, achieving the same level of support and efficiency as ARM and x86 in more user-centric applications—like your phone or computer—is an ongoing project. Industry trends, as evidenced by companies like SiFive and Ventana and initiatives like the RISE project, suggest a competitive and collaborative future for RISC-V. From a research standpoint, its open nature makes it a fertile ground for innovation across the entire hardware-to-software stack.There's a palpable sense of momentum behind RISC-V, with heavyweight stakeholders and top-tier companies pooling their resources and expertise. Far from slowing down, the ecosystem around RISC-V is only gaining speed, setting it on a course to become as ubiquitous and "boring" as the microprocessors that have come to define modern life.&nbsp;So, looking at where things are going, ignoring RISC-V would be like missing the boat when we switched from those giant mainframes to desktop PCs. It's a game-changer, and you really don't want to be left behind.

RISC-V Evolution: From Early Computing to Tomorrow's Tech Promise 

Harnessing the RISC-V Wave: The Future is Now

This is the first in a series of articles, from Synopsys that look at&nbsp;<a href="https://www.wevolver.com/article/designing-energy-efficient-ai-accelerators-for-data-centers-and-the-intelligent-edge" target="_blank" rel="noopener noreferrer">AI</a>, automotive, and multi-die chip design.&nbsp;<h2>Introduction</h2>According to Allied Market Research, the global artificial intelligence (AI) chip market is projected to reach $263.6 billion by 2031. The AI chip market is vast and can be segmented in a variety of different ways, including chip type, processing type, technology, application, industry vertical, and more. However, the two main areas where AI chips are being used are at the edge (such as the chips that power your phone and smartwatch) and in data centers (for deep learning inference and training).No matter the application, however, all <a href="https://www.synopsys.com/ai/ai-powered-eda.html" target="_blank">AI chips</a> can be defined as <a href="https://www.synopsys.com/glossary/what-is-ic-design.html" target="_blank">integrated circuits (ICs)</a> that have been engineered to run machine learning workloads and may consist of FPGAs, GPUs, or custom-built ASIC AI accelerators. They work very much like how our human brains operate and process decisions and tasks in our complicated and fast-moving world. The true differentiator between a traditional chip and an AI chip is how much and what type of data it can process and how many calculations it can do at the same time. At the same time, new software AI algorithmic breakthroughs are driving new AI chip architectures to enable efficient deep learning computation.Read on to learn more about the unique demands of AI, the many benefits of an AI chip architecture, and finally the applications and future of the AI chip architecture.<section id="isPasted"><h2>The Distinct Requirements of AI Chips</h2>The AI workload is so strenuous and demanding that the industry couldn’t efficiently and cost-effectively design AI chips before the 2010s due to the compute power it required—orders of magnitude more than traditional workloads. AI requires massive parallelism of multiply-accumulate functions such as dot product functions. Traditional GPUs were able to do parallelism in a similar way for graphics, so they were re-used for AI applications.The optimization we’ve seen in the last decade is drastic. AI requires a chip architecture with the right processors, arrays of memories, robust security, and reliable real-time data connectivity between sensors. Ultimately, the best AI chip architecture is the one that condenses the most compute elements and memory into a single chip. Today, we’re moving into multiple chip systems for AI as well since we are reaching the limits of what we can do on one chip.Chip designers need to take into account parameters called weights and activations as they design for the maximum size of the activation value. Looking ahead, being able to take into account both software and hardware design for AI is extremely important in order to optimize AI chip architecture for greater efficiency.</section><section><h2>The Benefits of AI Chip Architecture</h2>There’s no doubt that we are in the renaissance of AI. Now that we are overcoming the obstacles of designing chips that can handle the AI workload, there are many innovative companies that are experts in the field and designing better AI chips to do things that would have seemed very much out of reach a decade ago.As you move down process nodes, AI chip designs can result in 15 to 20% less clocking speed and 15 to 30% more density, which allows designers to fit more compute elements on a chip. They also increase memory components that allow AI technology to be trained in minutes vs. hours, which translates into substantial savings. This is especially true when companies are renting space from an online data center to design AI chips, but even those using in-house resources can benefit by conducting trial and error much more effectively.We are now at the point where AI itself is being used to design new AI chip architectures and calculate new optimization paths to optimize power, performance, and area (PPA) based on big data from many different industries and applications.</section><section><h2>AI Chip Architecture Applications and the Future Ahead</h2>AI is all around us quite literally. AI processors are being put into almost every type of chip, from the smallest IoT chips to the largest servers, data centers, and graphic accelerators. The industries that require higher performance will of course utilize AI chip architecture more, but as AI chips become cheaper to produce, we will begin to see AI chip architecture in places like IoT to optimize power and other types of optimizations that we may not even know are possible yet.It’s an exciting time for AI chip architecture. Synopsys predicts that we’ll continue to see next-generation process nodes adopted aggressively because of the performance needs. Additionally, there’s already much exploration around different types of memory as well as different types of processor technologies and the software components that go along with each of these.In terms of memory, chip designers are beginning to put memory right next to or even within the actual computing elements of the hardware to make processing time much faster. Additionally, software is driving the hardware, meaning that software AI models such as new neural networks are requiring new AI chip architectures. Proven, real-time interfaces deliver the data connectivity required with high speed and low latency, while security protects the overall systems and their data.Finally, we’ll see photonics and multi-die systems come more into play for new AI chip architectures to overcome some of the AI chip bottlenecks. Photonics provides a much more power-efficient way to do computing and multi-die systems (which involve the heterogeneous integration of dies, often with memory stacked directly on top of compute boards) can also improve performance as the possible connection speed between different processing elements and between processing and memory units increases.One thing is for sure: Innovations in AI chip architecture will continue to abound, and Synopsys will have a front-row seat and a hand in them as we help our customers design next-generation AI chips in an array of industries.Read more about how <a href="https://wevlv.co/synopsys-chip-arch" target="_blank" rel="noopener noreferrer">Synopsys is tackling AI chip architecture challenges</a>.</section>

AI chips, designed for machine learning tasks, are spurring new chip architectures for efficient deep learning computation. 

Why AI Requires a New Chip Architecture

<h2 dir="ltr" id="isPasted">Introduction</h2>Apache Kafka is a popular open-source distributed streaming platform that was developed by the Apache Software Foundation (Kafka, 2023). It is used to build real-time data pipelines and streaming applications that handle large volumes of data. Apache Kafka is designed to provide scalable, reliable, and fast data services making it a popular choice for data engineers and software developers. In this blog post, we will discuss what Apache Kafka is, how it works, and its pros and cons especially for the autonomous vehicle industry.<h2 dir="ltr">What is Apache Kafka?</h2>Apache Kafka is a distributed streaming platform that was first released in 2011. It is built on top of the Apache ZooKeeper, which is a centralized service for maintaining configuration information, naming, providing distributed synchronization, and providing group services. It is designed to handle high volumes of data and provide real-time access to that data.Apache Kafka is a publish-subscribe-based messaging system, which means that producers publish messages to topics, and consumers subscribe to those topics to receive messages. It also provides support for multiple consumer groups, which allows consumers to read messages in parallel. This makes it a highly scalable messaging system that can handle large volumes of data with low latency.<h2 dir="ltr">Advantages of Apache Kafka</h2>Apache Kafka has several advantages over traditional messaging systems.&nbsp;Scalability:&nbsp;Apache Kafka is highly scalable and can handle large volumes of data. Kafka can reliably process and store petabytes of data, making it a popular choice for large-scale data processing.&nbsp;Real-time Access to data:&nbsp;It provides real-time access to data, which means that consumers can receive data as soon as it is produced. This makes it ideal for real-time data processing applications, such as fraud detection, real-time analytics, and monitoring.&nbsp;Fault-tolerance:&nbsp;Apache Kafka is designed to handle failures and continue to operate without interruptions. Kafka provides a high level of durability, ensuring that data is never lost.&nbsp;Multiple consumer groups:&nbsp;This feature allows consumers from multiple groups to read messages in parallel. This makes it possible to process large volumes of data in real-time.&nbsp;<h2 dir="ltr">Disadvantages of Apache Kafka</h2>While Apache Kafka has several advantages, there are also many challenges to its successful integration with existing systems.&nbsp;Resource requirement:&nbsp;Apache Kafka requires a significant amount of resources, including memory and processing power. This can make it challenging to deploy on low-end hardware or in resource-constrained environments.&nbsp;Complexity:&nbsp;Apache Kafka presents a steep learning curve, and it may be difficult for beginners to learn and set up. Kafka requires a good understanding of distributed systems and messaging architectures, which can be challenging for new users.&nbsp;Scalability:&nbsp;It may not be suitable for small-scale applications or applications that do not require real-time data access. The overhead of setting up a Kafka cluster may outweigh the benefits for small-scale applications.<h2 dir="ltr">Apache Kafka and Uncrewed Vehicles</h2>Apache Kafka can provide numerous data streamlining applications to the uncrewed vehicles industry. Unmanned and autonomous vehicles are becoming widely used in a number of applications such as delivery, disaster relief, monitoring and mapping etc. They are routinely deployed to collect huge amounts of data through connected sensors and cameras. This data is then communicated to base stations and other vehicles. When operating in a remote or shared environment, unmanned vehicles need to share data with other entities as well. For example, in a disaster relief scenario, unmanned drones can be used to make 3D maps of the affected area, detect survivors and provide critical supplies. Huge amount of data is collected by swarms of unmanned vehicles and relief workers organize the best response based on this data. This is possible only when a robust data communication infrastructure exists in which the autonomous vehicles, their base stations, and the police and emergency departments of the area are involved.&nbsp;Apache Kafka can help in processing this data in real-time from uncrewed vehicles, such as drones, self-driving cars, and autonomous robots (Bear, 2017). The data can be processed in real-time, and the results can be used for further analysis and to make decisions that can help the vehicles navigate safely and avoid collisions. Apache Kafka was utilized to demonstrate a connected automotive infrastructure of 100,000 cars in 2020 (Waehner, 2020), (Waehner, 2020). Such an infrastructure can be extended to self-driving cars and other autonomous vehicles to streamline data.&nbsp;<h2 dir="ltr">Conclusion</h2>Apache Kafka is a distributed streaming platform that is used for building real-time data pipelines and streaming applications. It is highly scalable, fault-tolerant, and provides real-time access to data. However, it is a complex system of data streamlining hence, it presents a steep learning curve for beginners. It is ideal for large scale systems where resources such as memory and processing are not an issue. Unmanned vehicle applications where large amount of data is being collected and communicated in real time, offer perfect opportunities for the deployment of Apache Kafka.<hr><h2 dir="ltr">References</h2>Bear, J. W., 2017. IoT and the Autonomous Vehicle in the Clouds: SLAM with Kafka and Spark Streaming. Inside Machine Learning.Kafka, 2023. Apache Kafka. [Online]&nbsp; Available at: https://kafka.apache.org/Waehner, K., 2020. Apache Kafka in the Automotive Industry. [Online]&nbsp; Available at: https://www.kai-waehner.de/blog/2019/11/22/apache-kafka-automotive-industry-industrial-iot-iiot/Waehner, k., 2020. Streaming Machine Learning at Scale from 100000 IoT Devices with HiveMQ, Apache Kafka and TensorFLow. [Online]&nbsp; Available at: https://github.com/kaiwaehner/hivemq-mqtt-tensorflow-kafka-realtime-iot-machine-learning-training-inference

Apache Kafka is a popular open-source distributed streaming platform that was developed by the Apache Software Foundation (Kafka, 2023). It is used to build real-time data pipelines and streaming applications that handle large volumes of data.

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