⚡ 本页包含 AI 生成的分析内容，仅供参考
本文提出了一款基于7nm工艺的自适应计算加速平台处理器，旨在解决通用计算平台（如CPU、GPU）在摩尔定律放缓背景下的功耗效率问题。该处理器通过可重构异构计算架构，为边缘应用（如自动驾驶、机器人）和数据中心工作负载提供灵活的高带宽I/O与匹配的计算存储能力。
Fu-Hing Ho, Thomas To, Vamsi Nalluri, Mrinal Sarmah, Rajeev Patwari Xilinx, San Jose, CA As benefits from Moore’s Law diminish [1], general-purpose compute platforms like CPUs and GPUs continue to become increasingly power inefficient. The majority of workloads for emerging applications on the edge, like autonomous driving, sensor fusion, robotics, IoT, require flexible high-bandwidth I/O with matched compute and storage. Enterprise workload acceleration in the datacentres requires a heterogenous compute platform that provides the flexibility to be reconfigured in the field (e.g. to implement future neural-network innovations without hardware upgrade). Wireless 5G radios also require a similar mix of flexible I/O matched with high-performance math acceleration for efficient implementation of signal processors for MIMO systems. ACAP is a versatile new platform that addresses several of these domains with >130 INT8 TOPS for powerefficient neural-network implementations, and >100GSPS for
Prasun K. Raha, Tomai Knopp, Sagheer Ahmad, Ahmad Ansari,