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Is this something Akida could be helping with?
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equanimous
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I find it intersting that Ukraine has an interest in this site assuming it is correct
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BONSEYES – The Artificial Intelligence Marketplace
Future Challenges and Trends
Trust and explainability
AI algorithms, and especially deep neural networks, are often considered as black boxes, and as a consequence are not easily understandable for humans. The drawbacks of such algorithms can include a) any bias within the training data is potentially transferred to the algorithm and remains undetected, b) users may not trust their predictions, and c) that they may lack robustness in operational environments. Explainable AI is an active field of research that aims to provide insights into the internal decision-making process of machine learning algorithms. Using these insights, algorithms can be developed whose predictions are not only correct but right for the right reasons[1].
Re-learning
Considering the importance of edge AI, there is a need for commitment to consider the impact of edge AI throughout its lifecycle. To that extent, the developed algorithms must be kept up to date and performant on new data, with the ability to integrate external sources through re-training. In addition to meet the requirements and defined metrics that indicate the training state of the AI system, the re-training must also consider any con- sequence it may have on other components or the system itself. The implication is that rather than having to spend time and resources on re-training from scratch, to incorporate slightly different insights, the re-training should focus on creating more generic models. The aim is to permit improvements in performance through a quick re-training of an edge AI model that has already been trained using previous data sets. Equally, the re-training of one model should not compromise the performance of other components within the system (or other systems within a system of systems). In simple terms, the re-training must enable improved performance through exploitation of new data and in parallel it must not negatively impact its surroundings. Additionally, changes in calibration (e.g. of sensors or actuators) should be permitted without the need to retrain the edge AI.
Security and adversarial attacks
In distributed learning, a communication overhead is introduced in order for the edge platforms and the system aggregator to transfer data during training and inference. When compared to data processing in large central data centres, data produced on resource-constrained end devices in a decentralized and distributed setting is particularly vulnerable to security threats and the necessary level of protection against such risks should be considered carefully for specific applications. Further research is required to increase the security, privacy, and robustness of edge AI by reducing the overhead, or by adopting novel approaches such as clustered federated learning or federated distillations.
Learning at the Edge
Training artificial neural networks at the Edge remains a challenge. Work has been done to optimize inference at the Edge by optimizing algorithms and accelerators for low precision, low memory footprint and feed-for- ward computations. However, an additional re-training phase of an artificial neural network can undo part of those optimizations as higher precision is needed to enable the iterative approach typically used and more storage is needed to keep track of the intermediate data required. Also, the frequent weight updates during training can pose additional challenges regarding energy efficiency as well as reliability. As such, neuromorphic-based architectures hold potential, as they allow on-line learning to be built in by modelling plasticity. Plenty of challenges remain to achieve this goal as it is difficult to make a single synapse and neuron device that allows the capture of a very wide range of time constants.
Integrating AI into the smallest devices: Recently a number of tools have been developed with the goal of implementing AI models which could fit the memory available in edge platforms. As an example, tinyML is about processing sensor data at extremely low power and, in many cases, at the outer- most edge of the network. Therefore, tinyML applications could be deployed on the microcontroller in a sensor node to reduce the amount of data that the node forwards to the rest of the system. These integrated “tiny” machine learning applications require “full-stack” solutions (hardware, system, software, and applications) plus the machine learning architectures, techniques, and tools performing on-device analytics. Furthermore, a variety of sensing modalities (vision, audio, motion, environmental, human health monitoring, etc.) are used with extreme energy efficiency (typically in the single milliwatt, or lower, power range) to enable machine intelligence at the boundary of the physical and digital worlds. With the increase in dedicated hardware for machine learning, an important direction for future work is the development of compilers, such as Glow, and other tools that optimize neural network graphs for heterogeneous hardware or train and handle specialized technologies and algorithms.
Data centric AI
Data is the fundamental piece behind ML/AI. However, one of the major problems when developing AI solutions can be the lack of sufficient data to achieve the required performance in a specific application. In recent years several techniques have been considered to deal with this problem in the context of cloud-based solutions; for example, by using semi-supervised learning (to take advantage of the large amounts of unlabelled data generated by edge devices), by using data augmentation (via Generative Adversarial Networks (GANs) or transformations), or by transfer learning. These have become cutting-edge methods deployed to improve the overall performance in AI models. However, the adoption of these techniques in edge computing still needs to be thoroughly investigated. Moreover, edge systems need to interact with various types of IoT sensors, which produce a diversity of data such as image, text, sound, and motion. Edge analytics should be able to deal with those heterogeneous environments and adapt to be multimodal allowing learning from features collected over multiple modalities.
Neuromorphic technologies
Neuromorphic engineering is a ground-breaking approach to the design of computing technology that draws inspiration from powerful and efficient biological neural processing systems. Neuromorphic devices are able to carry out sensing, processing, and control strategies with ultra-low power performance. Today, the neuromorphic community in Europe is leading the State-of-the-Art in this domain. The community includes an increasing number of labs that work on the theory, modelling, and implementation of neuromorphic computing systems using conventional VLSI technologies, emerging memristive devices, photonics, spin-based, and other nano-technological solutions. Extensive work is needed in terms of neuromorphic algorithms, emerging technologies, hardware design and neuromorphic applications to enable the uptake of this technology, and to match the needs of real-world applications that solve real-world tasks in industry, health-care, assistive systems, and consumer devices. It is important to note that “neuromorphic” is most commonly defined as the group of brain-inspired hardware and algorithms.
Parallel to the advancement in neuromorphic computing, the underlying computation of such technology gets increasingly complex and requires more and more parameters. This triggers further development of efficient neuromorphic hardware designs, e.g. the development of neuromorphic hardware that can tackle the well- known memory wall issues and limited power budget in order to make such technology applicable on edge de- vices. The emerging memory technologies provide additional benefits for neuromorphic solutions, especially memory technology that can allow us to perform computation directly in the memory cells themselves instead of having to load and store the parameters, inputs, and outputs into computation cores.
Such technology, coupled with the properties of neuromorphic computing, delivers many benefits. Firstly, DL and spiking neural networks (SNN) parameters are often fixed and/or modified very seldom. This matches the capability of emerging non-volatile memories where write accesses are typically one or two orders slower than read accesses as the number of memory writes required is lower. Secondly, most computations are matrix addition and multiplication. This operation can be mapped efficiently in memory arrays. Thirdly, inference of such neuromorphic networks can be optimized for low-bit precision and coarse quantization without sacrificing the quality of the network outputs. Some tasks, such as classification, are proven to be good enough even when networks are optimized to binary and/or ternary representation. This provides an excellent opportunity as the underlying operation can be simply replaced by AND/XOR logic. Fourthly, neural networks are robust to error. Thus, process variations on the emerging memory technologies do not limit their capability to compute and/ or and load/store in the networks. These benefits can be achieved by in-memory compute technology using emerging memory technologies.
Meta-learning
In most of today’s industrial applications of deep learning, models and related learning algorithms are tailor-made for very specific tasks[2][3]. This procedure can lead to accurate solutions of complex and multidimensional problems but it also has visible weaknesses[4][5]. Normally, these models require an enormous amount of data to be able to learn how to correctly solve problems. Labelled data can be costly as it may require the intervention of experts or not be available in real-time applications due to the lack of generation events. A question can therefore arise: in addition to having the correct formulation and the descriptive data for the problem, is it possible not only to try to solve it but also to learn how to solve it in the best way? Therefore: “is it possible to learn how to learn?” Precisely on this question, the branch of machine learning, called meta-learning (Meta-L), is based[7][8]. In Meta-L the optimization is performed on multiple learning examples that consider different learning objectives in a series of training steps. In base learning, an inner learning algorithm, given a dataset and a target, solves a specific task such as image recognition. During meta learning, an outer algorithm updates the internal algorithm so that the model learned during base learning also optimizes an outer objective, which tries, for example, to increase the inner algorithm’s robustness or its generalization performance[9].
Intelligent extraction of information, by addressing the problem from a general point of view can also lead to the ability of the inner algorithm to handle new situations quickly and with little data available with a robust approach[10]. Looking at the advantages of Meta-Learning and the possibility of using it together with Edge computing to increase its benefits, provides a good outline of how this branch of ML can soon find concrete uses in the most varied application scenarios[11].
Hybrid modelling
Data-based and knowledge-based modelling can be combined into hybrid modelling approaches. Some solutions can take advantage of a-priori knowledge in the form of physical equations describing known causal relationships in the behaviour of the systems or by using well known simulation techniques. Whereas dependencies not known a priori can be represented by many kinds of machine learning methods using big data based on observing the behaviour of the systems. The former type of situation can be seen as white box modelling as the internal states possess a physical meaning, while the latter is referred to as black box modelling, using just the input-output-behaviour, but not maintaining information on the internal physical states of the system. However, in many cases, a model is not purely physics-based nor purely data-driven, giving rise to grey box modelling methods that can be formulated[12]. The assignment of models to the scale varies within the literature: For instance, a transfer function can be derived from physical considerations (white), identified from measurement data with a well-educated guess of the model order (grey) or without (black).
Approaches for combining machine learning and simulation, by simulation-assisted machine learning or by ma- chine-learning-assisted simulation and combinations are described by von Rueden et al. in “Combining Machine Learning and Simulation to a Hybrid Modelling approach: Current and Future Directions”[13] and in “Informed machine learning – towards a taxonomy of explicit integration of knowledge into machine learning.”[14] advantage of hybrid modelling is avoiding the necessity of learning a-priori the behaviour of systems from huge amounts of data, if they can be described by simulation techniques. Also, in the case of missing data, hybrid modelling is a possible approach[15].
A practical example of combining physical white-box modelling and machine learning to improve a model for the highly non-linear dynamic behaviour of a ship, described by a set of analytical equations has been recently investigated by Mei et al.[16]. Another example is hybrid modelling in process industries[17].
Energy efficiency
Reducing energy consumption is a general goal, not only, but especially for smart systems providers to address the challenges of global warming and enable a higher degree of miniaturization of intelligent devices. For a long time power reduction has been a challenge in micro and nano electronics and also a target for all AI applications, regardless of whether data is processed in the cloud or at the edge. But at the edge, this target is especially important as applications usually have only limited power resources available. They often have to be battery powered or even use energy harvesting.
Special energy-efficient neural network architectures have been investigated[56]. Not only is the hardware crucial for low-power AI applications, but also the implemented methods and models have great influence on the energy consumption. This has been examined for the example of computer vision[18].
Moving away from traditional von Neumann processing solutions and using dedicated hardware[19] allows for additional power reduction. Even more can be achieved with neuromorphic architectures[20].
The “ultimate benchmark” in power consumption for artificial intelligence would be the “natural intelligence” in form of the human brain, which has 86 bn. neurons[21] and approximately 1014–1015 synapses[22] with an energy consumption of less than 20W, based on glucose available to the brain, or only 0.2W, when counting the ATP usage instead of glucose[23]. Current GPU based solutions with that complexity are far from this energy efficiency. There is obviously plenty of headroom for further development
equanimous
Norse clairvoyant shapeshifter goddess
equanimous
Norse clairvoyant shapeshifter goddess
What safety features will Flip the bird entail??
Vehicle: Please put both hands on the steering wheel.
Driver: F$#K off
Vehicle: dispenses a bar of soap from the visor
Vehicle: Please put both hands on the steering wheel.
Driver: F$#K off
Vehicle: dispenses a bar of soap from the visor
Fact Finder
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That is very interesting as well as the absence of Germany from this group.
What I did find truly interesting was that compared with HC we have a much greater percentage of female to male posters/visitors.
If I had to guess this is the outcome I would have expected given the generally aggressive sexist abusive nature of HC.
Currently TSEx stands at 53% male 47% female and HC 67% male 33% female.
Would be great to see it become basically 50/50 at TSEx.
My opinion only DYOR
FF
AKIDA BALLISTA
Fact Finder
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Fact Finder
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In answer to his question it was the early 1960’s as I remember helping my father instal blinker lights to our Morris Minor so he could get through registration. Cannot imagine what modifications you could make to a new Tesla at home with an electric drill and a set of spanners.
FF
AKIDA BALLISTA
Fact Finder
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Great find. Pages 22 to 31 are directly relevant and worth reading.Dropbox
www.dropbox.com
Mention for BRN on page 28
My opinion only DYOR
FF
AKIDA BALLISTA
equanimous
Norse clairvoyant shapeshifter goddess
equanimous
Norse clairvoyant shapeshifter goddess
If you need to fix a broken down Tesla, you send a FordIn answer to his question it was the early 1960’s as I remember helping my father instal blinker lights to our Morris Minor so he could get through registration. Cannot imagine what modifications you could make to a new Tesla at home with an electric drill and a set of spanners.
FF
AKIDA BALLISTA
Realinfo
Regular
Although I have previously put this proposition to you, I would like to do so again.
There is a great deal of healthy discussion and debate about competition and what percentage of various markets we can expect. Man years are spent dot joining and considering whether this company or that application could involve Akida.
I would simply like to say this…it doesn’t matter what industry, what company or what application anyone here would like to nominate, it will only realise it’s full potential by using Akida.
Any other solution will be inferior at best, and very likely to condemn the organisation and those proposing it to the pathway of the dinosaurs.
My challenge to the 1000 eyes and any Doubting Thomas’s is…convince me that there is a reliable, resilient, proven alternative to Akida that will realistically appear anytime soon.
There is a great deal of healthy discussion and debate about competition and what percentage of various markets we can expect. Man years are spent dot joining and considering whether this company or that application could involve Akida.
I would simply like to say this…it doesn’t matter what industry, what company or what application anyone here would like to nominate, it will only realise it’s full potential by using Akida.
Any other solution will be inferior at best, and very likely to condemn the organisation and those proposing it to the pathway of the dinosaurs.
My challenge to the 1000 eyes and any Doubting Thomas’s is…convince me that there is a reliable, resilient, proven alternative to Akida that will realistically appear anytime soon.
equanimous
Norse clairvoyant shapeshifter goddess
Dang Son
Regular
Hi FF
Do you thinks these companies, referred to in the white paper along side BRN, are direct completion in our market?
GrAI Matter Labs | Fastest Edge AI Processor
GrAI Matter Labs has created the fastest edge AI processor for machine vision in robotics, AR/VR, drones and more. It achieves ultra-low latency at low power by leveraging sparsity.
www.graimatterlabs.ai
DYNAP Technology, Neuromorphic Solutions | SynSense
SynSense has low-power, low-latency neuromorphic solutions AI edge computing applications: autonomous robots, always-on co-processors for mobile, IoT applications etc.
www.synsense-neuromorphic.com
Fact Finder
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Yesterday @Diogenese posted an extract, some commentary and the following link:Hi FF
Do you thinks these companies, referred to in the white paper along side BRN, are direct completion in our market?
GrAI Matter Labs | Fastest Edge AI Processor
GrAI Matter Labs has created the fastest edge AI processor for machine vision in robotics, AR/VR, drones and more. It achieves ultra-low latency at low power by leveraging sparsity.Thanks in advanceDYNAP Technology, Neuromorphic Solutions | SynSense
SynSense has low-power, low-latency neuromorphic solutions AI edge computing applications: autonomous robots, always-on co-processors for mobile, IoT applications etc.
Espacenet – search results
Espacenet: free access to millions of patent documents. Find out if your invention is unique or if other inventors have filed patent applications that are considered to be prior art.
This patent protects AKIDA’s on chip learning.
Neither DYNAP or GrAi Matter have this ability nor do they have ONE SHOT LEARNING.
Neither do they have on chip convolution or SNN2CNN conversion.
Nor do they come in a package as IP allowing you to purchase a tiny bit of Ai or a massive stack of Ai.
On the question of are they competition yes but consider the Smart Light switch. Both you and I are competition to AKIDA because we can tell whether the light needs to be turned on or off.
Considering the above and assuming no price advantage which of the above technologies would you chose for your Smart Light switch if you decide not to compete.
My opinion only DYOR
FF
AKIDA BALLISTA
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SiFive Seeks to Fuel Next-Gen Designs with Automotive RISC-V Cores
one day ago by Jake Hertz
With its new portfolio of automotive RISC-V processor cores, SiFive aims to solve challenges in the design of evolving digital cars.
The RISC-V movement has been gaining some serious momentum this summer, finding value in a variety of fields and use cases. As a testament to this, the past week alone has seen the RISC-V movement has been bolstered by news from both Intel and NASA.
Now, that momentum is continuing again this week, this time with RISC-V finding its way into the automotive sector. This week, SiFive led the way along those lines with the announcement of a new portfolio of RISC-V processors designed to meet the demands of the next-generation automotive market.
In this article, we’ll look at the benefits of RISC-V for automotive as well as the new releases from SiFive.
Automotive Challenges
Today, the automotive industry is undergoing a revolution unlike any other time in history. The complete electrification and digitization of almost every facet of the automobile are creating smarter, safer, and more sustainable cars, but also come with a number of technical challenges.
Some of the many computing tasks within a modern automobile.
Some of the many computing tasks within a modern automobile. Image used courtesy of Johnson Automotive (Click image to enlarge)
One of the major challenges is the increased dependence on compute-intensive software and services within the car. For example, many of the applications within a vehicle, such as autonomous driving functions, rely on real-time processing for proper execution.
The result is that automotive designers are finding themselves in need of greater onboard computing capabilities in order to perform fast and reliable processing.
At the same time, next-generation automobiles simultaneously rely on a myriad of different computing tasks, ranging from infotainment systems, to wireless communications, to machine learning processing and computer vision. This broad range of applications makes it difficult to design a system that can perform all these tasks at a high level.
Together, these are driving the need for flexible and powerful computing hardware that is capable of supporting a variety of tasks without sacrificing performance or reliability.
RISC-V for Automotive
In the eyes of many, RISC-V presents a solution to many of these challenges. Some of the major benefits of RISC-V in the automotive space are the simplicity and performance that it brings to automotive design.
By using a single instruction set architecture (ISA) with RISC-V, a designer can create an automotive computing platform that enables a high level of code portability as well as decreased time to market. At the same time, many RISC-V offerings have been shown to offer high levels of performance as well as energy efficiency, making these devices ideal tools for automotive applications.
Beyond this, the RISC-V movement also provides designers with flexibility. Since RISC-V has grown globally, now consisting of thousands of members, there has emerged a wide variety of existing IP and resources on the market as well as the tools and information necessary to help designers create their own IP if needed.
SiFive’s Automotive RISC-V Portfolio
SiFive’s new portfolio of automotive-facing RISC-V offerings is part of the first phase of a long-term roadmap, says the company. The announcement describes three automotive solutions: the E6-A, X280-A, and S7-A.
The E6-A series is a 32-bit RISC-V processor that was designed specifically for real-time computing applications including system control and security. Built off of a single issue, in-order 8-stage Harvard Pipeline, and tightly integrated memory and cache subsystems, SiFive is describing the E6-A processors as offering mid-range power-efficient performance. SiFive tells us that the E6-A will be offered in ASIL A, B, and D safety levels and will be available by the end of 2022. The E6-A’s product page does not include a datasheet. It mentions a “Automotive E6-A Development Kit,” but with few details about it so far.
The E6-A RISC-V processor will support ASIL A, B, and D safety levels.
The E6-A RISC-V processor will support ASIL A, B, and D safety levels. Image used courtesy of SiFive
Following the E6-A, SiFive plans to release both the X280-A and S7-A by the second half of 2023. The X280-A will be a vector-capable processor that is optimized for sensor fusion, ADAS, and machine learning applications within the vehicle.
S7-A, on the other hand, will be tailored for applications such as ADAS, gateways, and domain controllers. To do this, the S7-A will be a real-time core that features native 64-bit support. As of now, there are no product pages on SiFive’s site for either the S7-A or the X280-A.
Together, SiFive hopes that its automotive line will provide automotive designers with the computing flexibility and performance needed to support the compute-intensive and varied tasks required by next-generation automobiles.
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SiFive Rolls Out Powerful New RISC-V Portfolio to Address Unmet Performance and Feature Needs of Rapidly Evolving Next-Gen Digital Automobiles
SiFive introduces Automotive E6-A, X280-A, and S7-A products and long-term roadmap
San Mateo, Calif. , September 13, 2022 - SiFive, Inc. the founder and leader of RISC-V computing, today announced three products as part of the first phase of a long-term roadmap and portfolio designed to meet the specific needs of automotive manufacturers and their suppliers. SiFive Automotive™ E6-A, X280-A, and S7-A solutions address critical needs for current and future applications like infotainment, cockpit, connectivity, ADAS, and electrification, as the market transitions to zonal architectures and manufacturers require the energy efficiency, simplicity, security, and software flexibility that RISC-V offers. SiFive's high-end applications and real-time processors offer industry-leading performance, with the lowest area and power consumption, and are being tailored to vehicle specific needs for safety, security, and performance. The company also highlighted a growing list of top-tier customers and leading ecosystem partners, and how they are collaborating to deliver comprehensive automotive solutions.
The transformation to the digital automobile has changed, accelerated, and increased the demands on computing requirements, the pace of innovation, and the flexibility required within the automotive supply ecosystem. This rapid evolution is driving the success of RISC-V in the automotive market, which is taking advantage of its flexible, modern architecture, fast growing ecosystem, and proven power and performance benefits to bring solutions that accelerate the pace of innovation in the vehicle space.
“SiFive is combining the best RISC-V benefits in the only end-to-end portfolio designed to meet automotive needs today and long into the future,” said Patrick Little, CEO and Chairman, SiFive. “We are seeing widespread interest in our new RISC-V automotive solutions and are working closely with several leading semiconductor companies and top-tier suppliers, who are turning to the flexibility of our highest performance cores in areas like safety-critical compute applications. Customers are now able to take advantage of our latest, most powerful cores to bring exciting innovations to consumers.”
“Renesas has been closely collaborating with SiFive to bring the strong benefits of RISC-V to many of our products,” said Takeshi Kataoka, Senior Vice President and General Manager of the Automotive Solution Business Unit at Renesas. “RISC-V continues to gain momentum around the world, and we plan to leverage SiFive’s portfolio of automotive RISC-V products in our future automotive SoC solutions to meet the exacting demands of these global customers. Partnering with an innovation leader like SiFive is a logical step that allows us to fuel our growth and meet our customer’s evolving requirements.”
RISC-V: Meeting Automotive Requirements Today and Into the Future
RISC-V brings a host of benefits to the automotive industry. Using a single instruction set architecture (ISA) across all product offerings – from safety islands to real-time products to highest performance ADAS and central zone compute – increases code portability and can greatly reduce cost and time-to-market, while RISC-V vector extensions bring enhanced machine learning and DSP capabilities. The global RISC-V ecosystem is growing rapidly, particularly in the U.S. and China, and consists of more than three thousand members. This provides wide choice and ensures future support and innovation. Working without proprietary lock-in, companies can license from multiple vendors and have more flexibility to design their own IP where needed, while maintaining software and ecosystem compatibility.
"Semiconductors are rapidly becoming the most critical component to the next generation of vehicles. With chips set to be over 20% of the BoM for cars by the end of the decade, the development of power-efficient, highly performant chips will continue to be a key priority for automotive manufacturers seeking to develop the most innovative automobiles for the future,” said Daniel Newman, Principal Analyst for Futurum Research. “SiFive continues to play an important role in leading the evolution of chip designs based on RISC-V, which I expect will gain market momentum as auto manufacturers seek to level up their vehicles to meet the increased demands of consumers wanting the most forward thinking, safe, and secure technology in their cars."
The company is also working with a growing list of ecosystem partners to build a comprehensive RISC-V automotive solution set. Check out the partner list here.. Also see the list of partner quotes below.
The SiFive Automotive E6-A, S7-A, and X280-A Series of Processors
The SiFive Automotive processor family offers the highest level of flexibility from any CPU IP vendor, with options that enable both area and performance optimization for different integrity levels like ASIL B, ASIL D, or mixed criticalities with split-lock, in line with ISO26262.
E6-A series for a variety of real-time, 32-bit applications, from system control to hardware security modules (HSMs) and safety islands, and as standalone in microcontrollers.
S7-A is a 64-bit, high-performance real-time core perfectly suited to the needs of modern SoCs with performant safety islands, requiring both low latency interrupt support and the same 64b memory space visibility as the main application CPUs.
X280-A builds on the successful performance and power efficiency of the X280 and is ideal for sensors, sensor fusion, and other vector or ML intensive workloads in automotive applications.
The SiFive Automotive family will also expand its portfolio in 2023 with a very high performance, out of order, application CPU with best-in-class performance and automotive capabilities.
SiFive's automotive products are accompanied by relevant safety packages that include documentation to accelerate the integration of the Safety Element out of Context (SEooC) and, with it, our customers’ time-to-market. Independent assessments of ASIL claims will support SiFive's safety claims.
Enabling solutions include proven SiFive WorldGuard security solutions. With tailored levels of integrity for functional safety, SiFive Automotive products are also compliant with relevant cybersecurity standards, such as WP.29, R155, and ISO21434.
Future Automobiles will be Powered by RISC-V
SiFive is creating a complete lineup of compute IP for MCUs, MPUs, and high-performance SoCs, as well as vector processing solutions tailored for automotive applications, with the first high-performance, out-of-order, Automotive family cores planned for late 2023.
"We are making a significant long-term investment into the future of automotive, and we are continuing to assemble a world-class team of automotive CPU design experts who are collaborating with industry leaders to drive the digital vehicle forward," continued Little.
From technologists like Monia Chiavacci who is a globally recognized pioneer in Functional Safety (FuSA) applied to systems-on-a-chip, a critical auto safety innovation, to Chairman and CEO, Patrick Little, who helped build the successful automotive business at Qualcomm, SiFive has put in place the team and tools to advance the digital automobile with RISC-V.
"One of the things that brought me to a leader like SiFive was the incredible potential of RISC-V, enabled by SiFive's innovations, to develop solutions for some of the automotive industry’s greatest challenges while ensuring flexibility and faster time to market for both suppliers and manufacturers," said Monia Chiavacci, SiFive Senior Principal Architect. "In addition to delivering our powerful processors, we are equally focused on ensuring the highest levels of functional safety without compromising performance and innovation."
With several lead customers already, the SiFive Automotive E6 products will ship in Q4 of this year and the S7A and X280A are expected to be available shortly after.
Learn more about SiFive’s Automotive Family of Products here.
In addition to the announcement of our own new RISC-V automotive product portfolio, SiFive is pleased to have the support of a wide base of ecosystem partners who are collaborating closely with us as we work to create the industry’s most comprehensive automotive solutions offering.
Ashling Ashling’s toolchain and RISC-V have grown to be synonymous as the embedded market continues to move from general purpose chips to fully/semi-custom multi-core solutions. Ashling’s RiscFree™ toolchain offers full customization package that allows development of a comprehensive, multi-core, heterogeneous, SDK tool suite tailored and optimized for any RISC-V based IP or device. Since the early days of RISC-V, Ashling’s comprehensive debug and trace solutions have supported SiFive Essential™ processors, with strong customer adoption, and we have plans to support SiFive’s RISC-V processor roadmap.
“For more than thirty years, Ashling’s toolchain and its various trace development solutions, including Ashling’s VITRA trace debug product, have been used by leading automotive companies. We are happy to extend our collaboration with SiFive to join in their automotive initiative by adding support for new SiFive Automotive processor portfolio, including work already underway for the SiFive Automotive™ E6-A. We are confident our Ashling toolchain and trace solution will offer significant added value to customers adopting SiFive Automotive processors,” said Hugh O’Keeffe, CEO of Ashling.
Cadence “Cadence looks forward to collaborating with SiFive on an automotive reference flow that utilizes our industry-leading digital, custom and verification solutions to enable mutual customers to design and deliver their SoCs quickly with optimal power, performance and area,” said KT Moore, vice president, Corporate Marketing, Cadence
Canonical “Canonical is thrilled to collaborate with SiFive in co-creating automotive solutions. With the advent of autonomous and connected cars, open-source software has become essential in fueling innovation in the automotive industry,” said Gordan Markus, silicon alliances partner manager, Canonical. “With the growing need to manage hardware and software complexity, Canonical and SiFive are perfectly positioned to allow our partners to bring efficient and performant automotive solutions to market at an accelerated pace. Furthermore, Ubuntu provides our partners with development simplicity, while ensuring enterprise-grade support and security.”
Elektrobit “Elektrobit is a leading provider of software solutions and services for the automotive industry with years of deep expertise in developing safety-critical applications to the highest standards,” said Mike Robertson, vice president, global product management and strategy, Elektrobit. “We see RISC-V building momentum in processor IP. As the automotive market continues to grow and evolve, Elektrobit is excited about the opportunities to develop applications based on SiFive’s extensive roadmap of RISC-V Automotive processors.”
Green Hills “As a global leader in embedded software with the broadest portfolio of ASIL D certified software solutions for 32-bit MCU to 64-bit MPUs, Green Hills is excited to be supporting SiFive’s impressive range of automotive-focused RISC-V CPU IP,” said Dan Mender, Vice President, Business Development, Green Hills Software. “To complement this remarkable new SiFive Automotive portfolio, Green Hills brings its unique ability to deliver MCU-to-MPU production-proven FuSa-certified tools, C/C++ compilers and RTOSes, along with decades of safety program expertise.”
IAR Systems “SiFive is a leading provider in the RISC-V ecosystem and has a long-standing relationship with IAR Systems. We are equally both excited and committed to supporting their increased focus on the Automotive-vertical,” said Anders Holmberg, CTO at IAR Systems. “The combination of innovative Automotive Functional Safety IP from SiFive and the certified development tools from IAR Systems is a perfect match. Building on IAR Systems’ 20+ years of experience supporting Functional Safety use cases, and the tens of thousands of developers using our products, there is now a true better-together offering to accelerate innovation in automobiles.”
iSystem AG “We are working with SiFive and other ecosystem partners on early support of RISC-V cores as we see them as an important contender in the future automotive market,” said Erol Simsek, CEO of iSYSTEM AG. “Our tools are designed to verify stringent safety requirements for automotive electronics, and a close cooperation with SiFive ensures that the very first device will offer all the necessary debug and real-time trace capabilities. Early adopters looking to evaluate RISC-V architecture can already use iSYSTEM tools to do so on existing SiFive devices or pre-silicon FPGA platforms.”
Lauterbach “It has always been a priority for Lauterbach to work closely with innovation leaders like SiFive to provide our customers with proven tools as soon as they are needed,” said Markus Herdin, head of marketing at Lauterbach. “We see the automotive industry as one of the big growth areas for RISC-V and are here to help with debug and trace solutions that meet the specific needs of this industry. We believe that with SiFive’s E6-A series, RISC-V will gain further momentum in automotive applications, which we will be delighted to support with our tools.”
Resiltech “Resiltech, aware of the importance of the role of RISC-V products and its ecosystems for the next generation automotive applications, is fully committed to confirm long-term support to SiFive to enable compliance of its automotive IP with the highest automotive safety requirements.” – Dr. Rosaria Esposito, CEO, Resiltech s.r.l.
SEGGER “SEGGER has been supporting RISC-V since 2017, and we support the complete range of RV32 and RV64 cores from SiFive,” said Rolf Segger, founder of SEGGER. “The SEGGER Software Platform – including the Embedded Studio IDE, the J-Link debug probes, as well as our embOS RTOS and associated middleware – provides a comprehensive one-stop solution for complete product development with microcontrollers based on the RISC-V architecture. We are excited to be part of SiFive’s Automotive initiative, and we are looking forward to supporting the E6-A product series in the near future.”
Siemens “Siemens Digital Industries Software has a long history of offering leading embedded software solutions for the automotive industry and is excited to extend our existing partnership with SiFive to enable even greater innovation in future automotive products,” said Jeff Hancock, Sr. Product Manager, Siemens Digital Industries Software.
Solid Sands Over the past few years, we have seen accelerated adoption of RISC-V worldwide. What surprises us is the speed of this also happening in the safety-critical automotive market, which is known to be conservative. Which implies that SiFive, with its RISC-V solution, solves a problem that is hard to crack. We are happy to assist SiFive customers with compiler and library automotive qualifications and see no inherent roadblocks to prevent open hardware and software from being used in safety-critical applications. - Marcel Beemster, CTO - Solid Sands
Synopsys “In the era of software-defined vehicles, Synopsys is helping to drive safety, security, reliability, and quality in the automotive digital value chain,” said Kiran Vittal, senior director of marketing in the Silicon Realization Group at Synopsys. “By collaborating with SiFive, we are enabling mutual customers to leverage our EDA design and verification solutions to achieve the optimal performance, power, area, and prototyping efficiency, while accelerating automotive-compliance for their RISC-V designs.”
SYSGO “SYSGO is proud to be a leading partner of SiFive with support for SiFive’s portfolio of RISC-V processors,” said Franz Walkembach, VP Marketing & Alliances at SYSGO. “With our certifiable hard real-time PikeOS operating system and hypervisor software combined with extensive technology expertise in functional safety, we will further support SiFive’s RISC-V solutions in markets such as Automotive, Space, Railway and Avionics.”
TASKING “As a trusted supplier to the automotive industry, TASKING is pleased to support the market introduction of SiFive Automotive processors. The TASKING® VX-toolset for RISC-V is a complete solution for code development for RISC-V based automotive ECUs. The VX-toolset for RISC-V produces fast and compact code and is being certified according to ISO 26262 functional safety and ISO/SAE 21434 cybersecurity standards,” said Gerard Vink, RISC-V Product Line Responsible, Tasking.
VIRTUAL OPEN SYSTEMS "At Virtual Open Systems (VOSyS) we are excited to port our ISO26260 certifiable mixed criticality virtualization solution VOSySmonitoRV to the SiFive automotive product family. The work at VOSyS side is well started, and we have proven Linux OS with FreeRTOS co-execution; this activity continues, and we are proceeding with MISRA and functional coverage. The plan is to complete ASIL certification based on the S7-A processor series in 2023," said Daniel Raho, CEO
WITTENSTEIN “As RISC-V becomes more popular, WITTENSTEIN high integrity systems’ partnership with SiFive allows us to support customers with the most cutting-edge processors, said Stephen Ridley, Engineering Manager at WHIS. “We are excited to see the ever-expanding automotive portfolio of SiFive and look expectantly to what is to come. SiFive have made it possible to develop new hardware faster than ever before, a must in the evolving automotive market. SiFive's automotive offerings are a great fit with SAFERTOS, our safety critical RTOS. Together they make a compelling package for automotive. WHIS and SiFive look forward to a continuation of our close collaboration in the future.
About SiFive As the pioneers who introduced RISC-V to the world, SiFive is transforming the future of compute by bringing the limitless potential of RISC-V to the highest performance and most data-intensive applications in the world. SiFive’s unrivaled compute platforms have enabled leading technology companies around the world to innovate, optimize and deliver the most advanced solutions of tomorrow across every market segment of chip design, including artificial intelligence, machine learning, automotive, data center, mobile, and consumer. With SiFive, the future of RISC-V has no limits. For more information, please visit SiFive.com.
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Media Contacts
Allison DeLeo
Racepoint Global for SiFive,
SiFive@racepointglobal.com,
Tel.: +1 (415) 694-6711
David Miller,
Corporate Communications,
SiFive,
David.Miller@sifive.com
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