PIMCA: A 3.4-Mb Programmable In-Memory Computing Accelerator in 28nm for On-Chip DNN Inference

Shihui Yin, Bo Zhang, Minkyu Kim, Jyotishman Saikia, Soon-Chan Kwon, Sungmeen Myung, Hyunsoo Kim, Sang Joon Kim, Mingoo Seok, Jae-sun Seo
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引用次数: 19

Abstract

We present a programmable in-memory computing (IMC) accelerator integrating 108 capacitive-coupling-based IMC SRAM macros of a total size of 3.4 Mb, demonstrating one of the largest IMC hardware to date. We developed a custom ISA featuring IMC and SIMD functional units with hardware loop to support a range of deep neural network (DNN) layer types. The 28nm prototype chip achieves system-level peak energy-efficiency of 437 TOPS/W and peak throughput of 4.9 TOPS at 40MHz, 1V supply.
基于片上深度神经网络推理的34mb可编程内存计算加速器
我们提出了一个可编程内存计算(IMC)加速器,集成了108个总大小为3.4 Mb的基于电容耦合的IMC SRAM宏,展示了迄今为止最大的IMC硬件之一。我们开发了一个定制的ISA,具有IMC和SIMD功能单元,具有硬件环路,以支持一系列深度神经网络(DNN)层类型。28nm原型芯片在40MHz, 1V电源下实现了437 TOPS/W的系统级峰值能效和4.9 TOPS的峰值吞吐量。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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