{"title":"基于 14 纳米 MRAM 的多比特模拟内存计算,可对 72 个基于宏的加速器进行过程变化校准","authors":"Sungmeen Myung;Seok-Ju Yun;Minje Kim;Wooseok Yi;Jaehyuk Lee;Jangho An;Kyoung-Rog Lee;Chang-Woo Shin;Seungchul Jung;Soonwan Kwon","doi":"10.1109/LSSC.2024.3465595","DOIUrl":null,"url":null,"abstract":"This letter presents an analog in-memory computing (IMC) macro utilizing 14 nm MRAM technology. To facilitate energy-efficient high-throughput multiply accumulate (MAC) operations, a multi-bit weight is introduced using stacked magnetic tunnel junction architecture and an analog bit-parallel MAC (ABP-MAC) scheme is proposed. This approach delivers 3.3 times better TOPS/mm2 than the state-of-the-art MRAM-based IMC macro. Additionally, a comprehensive calibration technique significantly improves computational accuracy across 72 IMC macros. The proposed IMC macro achieves 18.29 TOPS/mm2 and 340.8 TOPS/W with 1-bit normalization and classification accuracy of 90.2% with the Google speech commands dataset.","PeriodicalId":13032,"journal":{"name":"IEEE Solid-State Circuits Letters","volume":"7 ","pages":"295-298"},"PeriodicalIF":2.2000,"publicationDate":"2024-09-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"A 14 nm MRAM-Based Multi-bit Analog In-Memory Computing With Process-Variation Calibration for 72 Macros-Based Accelerator\",\"authors\":\"Sungmeen Myung;Seok-Ju Yun;Minje Kim;Wooseok Yi;Jaehyuk Lee;Jangho An;Kyoung-Rog Lee;Chang-Woo Shin;Seungchul Jung;Soonwan Kwon\",\"doi\":\"10.1109/LSSC.2024.3465595\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"This letter presents an analog in-memory computing (IMC) macro utilizing 14 nm MRAM technology. To facilitate energy-efficient high-throughput multiply accumulate (MAC) operations, a multi-bit weight is introduced using stacked magnetic tunnel junction architecture and an analog bit-parallel MAC (ABP-MAC) scheme is proposed. This approach delivers 3.3 times better TOPS/mm2 than the state-of-the-art MRAM-based IMC macro. Additionally, a comprehensive calibration technique significantly improves computational accuracy across 72 IMC macros. The proposed IMC macro achieves 18.29 TOPS/mm2 and 340.8 TOPS/W with 1-bit normalization and classification accuracy of 90.2% with the Google speech commands dataset.\",\"PeriodicalId\":13032,\"journal\":{\"name\":\"IEEE Solid-State Circuits Letters\",\"volume\":\"7 \",\"pages\":\"295-298\"},\"PeriodicalIF\":2.2000,\"publicationDate\":\"2024-09-23\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"IEEE Solid-State Circuits Letters\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://ieeexplore.ieee.org/document/10685516/\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q3\",\"JCRName\":\"COMPUTER SCIENCE, HARDWARE & ARCHITECTURE\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"IEEE Solid-State Circuits Letters","FirstCategoryId":"1085","ListUrlMain":"https://ieeexplore.ieee.org/document/10685516/","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q3","JCRName":"COMPUTER SCIENCE, HARDWARE & ARCHITECTURE","Score":null,"Total":0}
A 14 nm MRAM-Based Multi-bit Analog In-Memory Computing With Process-Variation Calibration for 72 Macros-Based Accelerator
This letter presents an analog in-memory computing (IMC) macro utilizing 14 nm MRAM technology. To facilitate energy-efficient high-throughput multiply accumulate (MAC) operations, a multi-bit weight is introduced using stacked magnetic tunnel junction architecture and an analog bit-parallel MAC (ABP-MAC) scheme is proposed. This approach delivers 3.3 times better TOPS/mm2 than the state-of-the-art MRAM-based IMC macro. Additionally, a comprehensive calibration technique significantly improves computational accuracy across 72 IMC macros. The proposed IMC macro achieves 18.29 TOPS/mm2 and 340.8 TOPS/W with 1-bit normalization and classification accuracy of 90.2% with the Google speech commands dataset.