基于12T SRAM的CNN高精度低功耗卷积算子

Tae Seob Oh, Younggun Pu, Kangyoon Lee
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引用次数: 1

摘要

为了获得较高的精度,需要将各种权重存储在内存中。为此,本文提出了三态权重静态随机存取存储器(SRAM)。12T SRAM是传统10T SRAM上的一种功率门控形式。通过使用功率门控,可以关闭逆变器。当逆变器关闭时,新的权重(0)可以存储在12T SRAM中。在0.18µm CMOS工艺中制造的算子在卷积时功耗为172.3µW,电源为1.8V。即使没有调整大小,写入余量也比传统的SRAM好,精度提高了23.2%。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
A High Accuracy Low Power Convolution Operator with 12T SRAM for CNN
To get high accuracy, various weight should be stored in memory. For this, this paper presents tri-state weight static random access memory(SRAM). 12T SRAM is a form of power gating on the conventional 10T SRAM. By using power gating, the inverter can be turned off. The new weight (0) can be stored in 12T SRAM when inverter is turned off. The operator fabricated in a 0.18-µm CMOS process dissipates 172.3µW with the supply of 1.8V while convolution. Even without sizing, the writing margin is better than the conventional SRAM and the accuracy is improved by 23.2%.
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