一种结合CPU和深度学习的通用内存计算处理器，提高CPU效率和增强数据局域性

2023 IEEE Symposium on VLSI Technology and Circuits (VLSI Technology and Circuits) Pub Date : 2023-06-11 DOI:10.23919/VLSITechnologyandCir57934.2023.10185311

Yuhao Ju, Yijie Wei, X. Chen, Jie Gu

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引用次数: 0

摘要

本研究提出了一种结合DNN运算和向量CPU的通用内存计算(GPCIM)处理器。利用特殊的可重构性、数据流和指令集，65nm测试芯片具有28.5 TOPS/W的DNN宏效率和同类最佳的802GOPS/W峰值CPU效率。由于数据局域性流，通过消除核间数据传输，人工智能相关应用程序的端到端延迟改善了37%至55%。

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A General-Purpose Compute-in-Memory Processor Combining CPU and Deep Learning with Elevated CPU Efficiency and Enhanced Data Locality

This work presents a general-purpose compute-in-memory (GPCIM) processor combining DNN operations and vector CPU. Utilizing special reconfigurability, dataflow, and instruction set, the 65nm test chip demonstrates a 28.5 TOPS/W DNN macro efficiency and a best-in-class peak CPU efficiency of 802GOPS/W. Due to a data locality flow, 37% to 55% end-to-end latency improvement on AI-related applications is achieved by eliminating inter-core data transfer.

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2023 IEEE Symposium on VLSI Technology and Circuits (VLSI Technology and Circuits)

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