Kanglin Xiao, Xiaoxin Cui, Xin Qiao, Xin'an Wang, Yuan Wang
{"title":"支持乒乓操作的可重构SRAM内存计算宏和支持多模式MAC操作的CIM管道","authors":"Kanglin Xiao, Xiaoxin Cui, Xin Qiao, Xin'an Wang, Yuan Wang","doi":"10.1109/ICTA56932.2022.9962978","DOIUrl":null,"url":null,"abstract":"In this work, we present a reconfigurable SRAM computing-in-memory (CIM) macro supporting ping-pong operation and pipeline operation for multi-mode multiply-and-accumulate (MAC) operations. The macro can be reconfigured to execute AND or XNOR, offering great flexibilities to cover binary neural network (BNN), ternary neural network (TNN), and multi-bit operation through serially 1-bit AND operations. The main contributions include: (1) A reconfigurable scheme to map inputs and weight of 8T1C bit-cell, supporting three MAC operations; (2) An architecture integrated ping-pong operation and two-level CIM pipeline. Simulated in a standard 28-nm process, the proposed design shows good computing linearity and variations. The average energy efficiency of 1b-AND, BNN, and TNN MAC operation are 1533.7, 1522.9, and 1713.2 TOPS/W, respectively.","PeriodicalId":325602,"journal":{"name":"2022 IEEE International Conference on Integrated Circuits, Technologies and Applications (ICTA)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2022-10-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"A Reconfigurable SRAM Computing-in-Memory Macro Supporting Ping-Pong Operation and CIM pipeline for Multi-mode MAC operations\",\"authors\":\"Kanglin Xiao, Xiaoxin Cui, Xin Qiao, Xin'an Wang, Yuan Wang\",\"doi\":\"10.1109/ICTA56932.2022.9962978\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"In this work, we present a reconfigurable SRAM computing-in-memory (CIM) macro supporting ping-pong operation and pipeline operation for multi-mode multiply-and-accumulate (MAC) operations. The macro can be reconfigured to execute AND or XNOR, offering great flexibilities to cover binary neural network (BNN), ternary neural network (TNN), and multi-bit operation through serially 1-bit AND operations. The main contributions include: (1) A reconfigurable scheme to map inputs and weight of 8T1C bit-cell, supporting three MAC operations; (2) An architecture integrated ping-pong operation and two-level CIM pipeline. Simulated in a standard 28-nm process, the proposed design shows good computing linearity and variations. The average energy efficiency of 1b-AND, BNN, and TNN MAC operation are 1533.7, 1522.9, and 1713.2 TOPS/W, respectively.\",\"PeriodicalId\":325602,\"journal\":{\"name\":\"2022 IEEE International Conference on Integrated Circuits, Technologies and Applications (ICTA)\",\"volume\":\"1 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2022-10-28\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2022 IEEE International Conference on Integrated Circuits, Technologies and Applications (ICTA)\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/ICTA56932.2022.9962978\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2022 IEEE International Conference on Integrated Circuits, Technologies and Applications (ICTA)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/ICTA56932.2022.9962978","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
A Reconfigurable SRAM Computing-in-Memory Macro Supporting Ping-Pong Operation and CIM pipeline for Multi-mode MAC operations
In this work, we present a reconfigurable SRAM computing-in-memory (CIM) macro supporting ping-pong operation and pipeline operation for multi-mode multiply-and-accumulate (MAC) operations. The macro can be reconfigured to execute AND or XNOR, offering great flexibilities to cover binary neural network (BNN), ternary neural network (TNN), and multi-bit operation through serially 1-bit AND operations. The main contributions include: (1) A reconfigurable scheme to map inputs and weight of 8T1C bit-cell, supporting three MAC operations; (2) An architecture integrated ping-pong operation and two-level CIM pipeline. Simulated in a standard 28-nm process, the proposed design shows good computing linearity and variations. The average energy efficiency of 1b-AND, BNN, and TNN MAC operation are 1533.7, 1522.9, and 1713.2 TOPS/W, respectively.