{"title":"Specific ADC of NVM-Based Computation-in-Memory for Deep Neural Networks","authors":"Ao Shi;Yizhou Zhang;Lixia Han;Zheng Zhou;Yiyang Chen;Haozhang Yang;Lifeng Liu;Linxiao Shen;Xiaoyan Liu;Jinfeng Kang;Peng Huang","doi":"10.1109/TCSI.2024.3430290","DOIUrl":null,"url":null,"abstract":"Non-volatile memory (NVM)-based Computation-in-memory has demonstrated a significant advantage in high-efficiency neural networks. However, the requirement of analog-to-digital converter (ADC) and post-processing circuits not only cost high energy and area but also results in high computation errors, which tradeoffs the performance boost brought by CIM. Here, we present a specific ADC and post-processing circuit of the NVM-based CIM neural network to address these issues. The main contributions include: (1) A novel residual charge accumulation function (RCA) is designed to achieve charge-domain summation of quantized partial sum and reduces 38% quantization error; (2) Charge reset is introduced in the integrate & fire circuit to realize <1> <tex-math>$3.95\\times $ </tex-math></inline-formula>\n energy efficiency and \n<inline-formula> <tex-math>$2.48\\times $ </tex-math></inline-formula>\n area efficiency. Evaluation based on the measured results of the fabricated chip shows that the VGG-11 neural network with the proposed ADC circuit can achieve a 3.28-time improvement in energy efficiency while maintaining the same network recognition rate.","PeriodicalId":13039,"journal":{"name":"IEEE Transactions on Circuits and Systems I: Regular Papers","volume":"71 12","pages":"5387-5399"},"PeriodicalIF":5.2000,"publicationDate":"2024-09-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"IEEE Transactions on Circuits and Systems I: Regular Papers","FirstCategoryId":"5","ListUrlMain":"https://ieeexplore.ieee.org/document/10668403/","RegionNum":1,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"ENGINEERING, ELECTRICAL & ELECTRONIC","Score":null,"Total":0}
引用次数: 0
Abstract
Non-volatile memory (NVM)-based Computation-in-memory has demonstrated a significant advantage in high-efficiency neural networks. However, the requirement of analog-to-digital converter (ADC) and post-processing circuits not only cost high energy and area but also results in high computation errors, which tradeoffs the performance boost brought by CIM. Here, we present a specific ADC and post-processing circuit of the NVM-based CIM neural network to address these issues. The main contributions include: (1) A novel residual charge accumulation function (RCA) is designed to achieve charge-domain summation of quantized partial sum and reduces 38% quantization error; (2) Charge reset is introduced in the integrate & fire circuit to realize <1> $3.95\times $
energy efficiency and
$2.48\times $
area efficiency. Evaluation based on the measured results of the fabricated chip shows that the VGG-11 neural network with the proposed ADC circuit can achieve a 3.28-time improvement in energy efficiency while maintaining the same network recognition rate.
期刊介绍:
TCAS I publishes regular papers in the field specified by the theory, analysis, design, and practical implementations of circuits, and the application of circuit techniques to systems and to signal processing. Included is the whole spectrum from basic scientific theory to industrial applications. The field of interest covered includes: - Circuits: Analog, Digital and Mixed Signal Circuits and Systems - Nonlinear Circuits and Systems, Integrated Sensors, MEMS and Systems on Chip, Nanoscale Circuits and Systems, Optoelectronic - Circuits and Systems, Power Electronics and Systems - Software for Analog-and-Logic Circuits and Systems - Control aspects of Circuits and Systems.