一句话：总结 AWS Trainium 从芯片到编译器、运行时全栈协同设计与优化的经验。
本文讨论机器学习加速器在芯片架构、指令集、服务器设计、编译器及片间/片内互连等方面面临的设计挑战。AWS Trainium 借助 AWS 从芯片、服务器、网络到编译器与运行时工具的端到端自主掌控，跨层协同设计与优化，并强调简洁性和易用性。演讲将阐述 AWS 三代机器学习产品从概念到交付客户过程中的设计原则、权衡取舍与经验教训。
Machine learning accelerators present a unique set of design challenges across chip architecture, instruction set, server design, compiler, and both inter- and intra-chip connectivity. With AWS Trainium, we've utilized AWS's end-to-end ownership from chip to server, network, compilers, and runtime tools to collaboratively design and optimize across all layers, emphasizing simplicity and ease of use. This talk will illustrate the design principles, tradeoffs, and lessons learned during the development of three generations of AWS ML products, from conceptualization to placing systems in the hands of AWS customers.