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Yesterday, Arm has announced significant progress in its Total Design initiative. The program, launched a year ago, aims to accelerate the development of custom silicon for data centers by fostering collaboration among industry partners. The ecosystem has now grown to include nearly 30 participating companies, with recent additions such as Alcor Micro, Egis, PUF Security, and SEMIFIVE. A notable development is a partnership between Arm, Samsung Foundry, ADTechnology, and Rebellions to create an AI CPU chiplet platform. This collaboration aims to deliver a solution for cloud, HPC, and AI/ML workloads, combining Rebellions' AI accelerator with ADTechnology's compute chiplet, implemented using Samsung Foundry's 2 nm Gate-All-Around (GAA) FET technology. The platform is expected to offer significant efficiency gains for generative AI workloads, with estimates suggesting a 2-3x improvement over the standard CPU design for LLMs like Llama3.1 with 405 billion parameters.
Arm's approach emphasizes the importance of CPU compute in supporting the complete AI stack, including data pre-processing, orchestration, and advanced techniques like Retrieval-augmented Generation (RAG). The company's Compute Subsystems (CSS) are designed to address these requirements, providing a foundation for partners to build diverse chiplet solutions. Several companies, including Alcor Micro and Alphawave, have already announced plans to develop CSS-powered chiplets for various AI and high-performance computing applications. The initiative also focuses on software readiness, ensuring that major frameworks and operating systems are compatible with Arm-based systems. Recent efforts include the introduction of Arm Kleidi technology, which optimizes CPU-based inference for open-source projects like PyTorch and Llama.cpp. Notably, as Google claims, most AI workloads are being inferenced on CPUs, so creating the most efficient and most performant CPUs for AI makes a lot of sense.
View at TechPowerUp Main Site | Source
Arm's approach emphasizes the importance of CPU compute in supporting the complete AI stack, including data pre-processing, orchestration, and advanced techniques like Retrieval-augmented Generation (RAG). The company's Compute Subsystems (CSS) are designed to address these requirements, providing a foundation for partners to build diverse chiplet solutions. Several companies, including Alcor Micro and Alphawave, have already announced plans to develop CSS-powered chiplets for various AI and high-performance computing applications. The initiative also focuses on software readiness, ensuring that major frameworks and operating systems are compatible with Arm-based systems. Recent efforts include the introduction of Arm Kleidi technology, which optimizes CPU-based inference for open-source projects like PyTorch and Llama.cpp. Notably, as Google claims, most AI workloads are being inferenced on CPUs, so creating the most efficient and most performant CPUs for AI makes a lot of sense.
View at TechPowerUp Main Site | Source
