@inproceedings{50a17baacb704fb190fa6627523b1984,
title = "A High Accuracy Low Power Convolution Operator with 12T SRAM for CNN",
abstract = "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\%.",
keywords = "12T SRAM, Convolution, convolutional neural networks (CNN), processing in memory (PIM), Tri-state weight",
author = "Oh, \{Tae Seob\} and Pu, \{Young Gun\} and Lee, \{Kang Yoon\}",
note = "Publisher Copyright: {\textcopyright} 2021 IEEE.; 12th International Conference on Ubiquitous and Future Networks, ICUFN 2021 ; Conference date: 17-08-2021 Through 20-08-2021",
year = "2021",
month = aug,
day = "17",
doi = "10.1109/ICUFN49451.2021.9528655",
language = "English",
series = "International Conference on Ubiquitous and Future Networks, ICUFN",
publisher = "IEEE Computer Society",
pages = "295--298",
booktitle = "ICUFN 2021 - 2021 12th International Conference on Ubiquitous and Future Networks",
}