Shenzhen, China

The increasingly competitive automotive industry presents significant challenges to the development of vehicle functions. All OEMs face the task of developing more, better, and cooler functions in a shorter time and at a lower cost. 

The traditional development model, which relies solely on expanding team size, is becoming less effective in meeting the demands for cost, efficiency, and quality. The automotive industry needs a more innovative, agile, flexible, and platform-based development model. To achieve that and drive innovation, it is essential to closely integrate development tools, development models, and vehicle architectures.

The use of ML and generative AI is rapidly shifting from hype into adoption, creating the need for more efficient inferencing at scale. Large language models are getting smaller and more specialized, offering comparable or improved performance at a fraction of the cost and energy. Advances in inferencing techniques, like quantization, sparse-coding, and the rise of specialized, lightweight frameworks like llama.cpp, enable LLMs to run on CPUs and provide good performance for a wide variety of use-cases. Arm has been advancing the capabilities of Neoverse cores to address both the compute and memory needs of LLMs, while maintaining its focus on efficiency. Popular ML frameworks like llama.cpp, PyTorch, and ML compilers allow easy migration of ML models to Arm-based cloud instances. These hardware and software improvements have led to an increase in on-CPU ML performance for use cases like LLMs and recommenders. This gives AI application developers flexibly in choosing CPUs or GPUs, depending on use case and sustainability targets. Arm is also making it possible for ML system designers to create their own bespoke ML solutions including accelerators combined with CPU chiplets to offer the best of both worlds.

Chip design has once again seen a paradigm shift. From the north and south bridges of the PC era, with the development of Moore's Law, all IPs were integrated into an SoC, and the chiplet architecture was born due to the development limit of Moore's Law. Egis/Alcor Micro will stand on the shoulders of two giants, Arm CSS V3 and TSMC, and combine the two key chiplet technologies - UCIe and CoWoS to launch the scalable, high-performance and most power-saving AI HPC Server solution. We will combine CPU chips, AI Accelerator chips and IO chips in a Lego-style, highly flexible form to create the AI ​​HPC Server product that best meets customer needs.

The rapid expansion of AI has led to a major shift in infrastructure technology. Arm Neoverse emerges as platform of choice, offering the best combination of performance, efficiency, and design flexibility. This roadmap session explores how Arm Neoverse forms the foundation for partner innovation across cloud, wireless, networking, HPC, and edge, enabling the deployment of performant and vastly more efficient AI infrastructure on Arm. From Neoverse IP products to the Arm Neoverse Compute Subsystems (CSS) and Arm Total Design, we show why Arm Neoverse is the platform of choice for industry leaders and how it is pivotal in accelerating and shaping the future of AI infrastructure.

LLMs are extensively utilized in natural language processing, intelligent assistants, and content generation, driving a growing demand for on-device inference capabilities on mobile devices. However, Arm-based mobile devices face significant challenges in efficiently deploying LLMs due to constraints in computational resources, power consumption, and memory capacity. This presentation delves into optimization techniques based on the MNN framework, aiming to enhance the performance and efficiency of LLM inference on Arm architecture mobile devices.

The latest Armv9 architecutre delivers industry-leading architecture enhancements to help increase compute capabilities with more AI performance for each generation, from Matmul and Neon, to SVE2.  Join this session for the inside track on enabling  more efficienct AI  compute for your next-gen solution. 

The presence of AI has been in vehicles for at least a decade, though recent advances have supported its pervasiveness. AI is foundational for improving in-vehicle user experiences and automated features, yet it brings a set of unique challenges not faced by other industries. Here we discuss how the future can become reality and where there are opportunities to solve some of the greatest challenges.

Arm’s mission for relentless innovation for CPU Architecture means that we’re never standing still. In this talk we will explore the architectural challenges of AI, and how our architecture features will deliver significant improvements for running such AI and related workloads.

Chiplets are offering automotive OEMs and suppliers more flexibility to customize their silicon as part of the shift to software-defined vehicles. As the industry embraces chiplet technology, it requires standards to ensure compatibility between chiplets from different providers and to create an easy-to-build platform. Here, we explore the excitement around chiplets in the automotive sector and Arm's role in supporting the development of standards and foundational compute platforms for its expansive ecosystem.

Virtual prototyping helps automotive semiconductor, tier 1, and OEM companies speed up development, boost productivity, cut costs and enhance hardware and software quality for future vehicles. This session explores the virtual prototyping offerings delivered by Arm partners using the latest Arm Automotive Enhanced (AE) technology, which can accelerate development cycles by up to two years.

Arm Compiler for Embedded (also known as AC6) is very widely used for software development for Arm-based products. Developed and supported by true Arm experts, combining early support for new Arm architectures and cores with highly competitive scores on key embedded benchmarks, and a safety-qualified variant for development of safety-critical systems, Arm Compiler for Embedded is THE professional embedded toolchain for Arm. We’re investing in significant changes for Arm Compiler for Embedded, to bring even more value to developers of Arm-based embedded products. These changes include POSIX support to enable use of Rich embedded Operating Systems, more security features for developers with interests in cyber-security and memory safety, and better compatibility with GCC. We’re also creating a free to use, 100% open source toolchain LLVM technology, with identical functionality and performance to our professional/commercial toolchain. Whichever compilation toolchain you currently use for Arm-based development, the changes discussed in this session are going to be of great interest to you.

The IoT ecosystem has already shipped billions of chips based on Arm, so its fair to say the IoT already runs on Arm. The IoT industry however, never stands still. We are seeing a rapid market acceleration that demands even higher performance solutions to deliver new and exciting use cases. We all therefore have to continue to innovate.

In this session, we present our hardware, software, and standards solutions that enable our customers and the entire Arm ecosystem to participate and win in this rapidly changing environment. Join us to learn more about the latest and greatest technology Arm is creating for the leaders in IoT to succeed.

1. CPU subsystem : Main CPU(Cortex-A76 Quad 2clusters)와 L1&L2 Cache, GIC(Generic Interrupt Controller) & Debugging subsystem(Coresight)

Generative AI solutions spanning diverse industries. Explore how Arm enables you to harness the complete potential of Generative AI at the edge, even on the smallest devices, revolutionizing your business and sculpting a smarter, more interconnected future.

KleidiAI is a set of micro-kernels that integrates into machine learning frameworks, accelerating AI inference on Arm-based platforms. These micro-kernels are hand-optimized in Arm assembly code to leverage modern architecture instructions, significantly speeding up AI inference on Arm CPUs. This presentation is an introductory topic for developers who are curious about how KleidiAI works and delivers such speedup.

Arm has worked with the Google AI Edge team to integrate KleidiAI into the MediaPipe framework through XNNPACK. These improvements increase the throughput of quantized LLMs running on Arm chips that contain the i8mm feature. This presentation will share new techniques for Android developers who want to efficiently run LLMs on-device.

AI at the edge is built on the foundation of robust framework of secure, connected devices to perform critical tasks in real-time. In this session, Linaro wants to stress the accent on the importance of a solution like ONELab, a solution that empowers businesses to build AI-driven edge devices that are secure, compliant, ready for deployment and faster to be launched on the market. With ONELab the full potential of AI at the edge for innovative applications has never been closer and easier.