Please use this identifier to cite or link to this item: https://hdl.handle.net/10356/163744
Title: A 65-nm 8T SRAM compute-in-memory macro with column ADCs for processing neural networks
Authors: Yu, Chengshuo
Yoo, Taegeun
Chai, Kevin Tshun Chuan
Kim, Tony Tae-Hyoung
Kim, Bongjin
Keywords: Engineering::Electrical and electronic engineering
Issue Date: 2022
Source: Yu, C., Yoo, T., Chai, K. T. C., Kim, T. T. & Kim, B. (2022). A 65-nm 8T SRAM compute-in-memory macro with column ADCs for processing neural networks. IEEE Journal of Solid-State Circuits, 57(11), 3466-3476. https://dx.doi.org/10.1109/JSSC.2022.3162602
Project: A18A7b0058 
Journal: IEEE Journal of Solid-State Circuits 
Abstract: In this work, we present a novel 8T static random access memory (SRAM)-based compute-in-memory (CIM) macro for processing neural networks with high energy efficiency. The proposed 8T bitcell is free from disturb issues thanks to the decoupled read channels by adding two extra transistors to the standard 6T bitcell. A 128 $\times $ 128 8T SRAM array offers massively parallel binary multiply and accumulate (MAC) operations with 64 $\times $ binary inputs (0/1) and 64 $\times $ 128 binary weights (+1/-1). After parallel MAC operations, 128 column-based neurons generate 128 $\times $ 1-5 bit outputs in parallel. The proposed column-based neuron comprises 64 $\times $ bitcells for dot-product, 32 $\times $ bitcells for analog-to-digital converter (ADC), and 32 $\times $ bitcells for offset calibration. The column ADC with 32 $\times $ replica SRAM bitcells converts the analog MAC results (i.e., a differential read bitline (RBL/RBLb) voltage) to the 1-5 bit output code by sweeping their reference levels in 1-31 cycles (i.e., $2^{N}$ -1 cycles for $N$ -bit ADC). The measured linearity results [differential nonlinearity (DNL) and integral nonlinearity (INL)] are +0.314/-0.256 least significant bit (LSB) and + 0.27/-0.116 LSB, respectively, after offset calibration. The simulated image classification results are 96.37% for Mixed National Institute of Standards and Technology database (MNIST) using a multi-layer perceptron (MLP) with two hidden layers, 87.1%/82.66% for CIFAR-10 using VGG-like/ResNet-18 convolutional neural networks (CNNs), demonstrating slight accuracy degradations (0.67%-1.34%) compared with the software baseline. A test chip with a 16K 8T SRAM bitcell array is fabricated using a 65-nm process. The measured energy efficiency is 490-15.8 TOPS/W for 1-5 bit ADC resolution using 0.45-/0.8-V core supply.
URI: https://hdl.handle.net/10356/163744
ISSN: 0018-9200
DOI: 10.1109/JSSC.2022.3162602
Schools: School of Electrical and Electronic Engineering 
Rights: © 2022 IEEE. All rights reserved.
Fulltext Permission: none
Fulltext Availability: No Fulltext
Appears in Collections:EEE Journal Articles

SCOPUSTM   
Citations 20

25
Updated on Apr 20, 2024

Web of ScienceTM
Citations 20

11
Updated on Oct 30, 2023

Page view(s)

168
Updated on Apr 23, 2024

Google ScholarTM

Check

Altmetric


Plumx

Items in DR-NTU are protected by copyright, with all rights reserved, unless otherwise indicated.