{"title":"一个6.54到26.03 TOPS/W的内存中计算RNN处理器，使用输入相似性优化和基于注意力的上下文分解和输出推测","authors":"Ruiqi Guo, Hao Li, Ruhui Liu, Zhixiao Zhang, Limei Tang, Hao Sun, Leibo Liu, Meng-Fan Chang, Shaojun Wei, S. Yin","doi":"10.23919/VLSICircuits52068.2021.9492492","DOIUrl":null,"url":null,"abstract":"This work presents a 65nm RNN processor with computing-inmemory (CIM) macros. The main contributions include: 1) A similarity analyzer (SimAyz) to fully leverage the temporal stability of input sequences with 1.52× performance speedup; 2) An attention-based context-breaking (AttenBrk) method with output speculation to reduce off-chip data accesses up to 30.3%; 3) A double-buffering scheme for CIM macros to hide writing latency and a pipeline processing element (PE) array to increase the system throughput. Measured results show that this chip achieves 6.54-to-26.03 TOPS/W energy efficiency vary from various LSTM benchmarks.","PeriodicalId":106356,"journal":{"name":"2021 Symposium on VLSI Circuits","volume":"1 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2021-06-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"4","resultStr":"{\"title\":\"A 6.54-to-26.03 TOPS/W Computing-In-Memory RNN Processor using Input Similarity Optimization and Attention-based Context-breaking with Output Speculation\",\"authors\":\"Ruiqi Guo, Hao Li, Ruhui Liu, Zhixiao Zhang, Limei Tang, Hao Sun, Leibo Liu, Meng-Fan Chang, Shaojun Wei, S. Yin\",\"doi\":\"10.23919/VLSICircuits52068.2021.9492492\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"This work presents a 65nm RNN processor with computing-inmemory (CIM) macros. The main contributions include: 1) A similarity analyzer (SimAyz) to fully leverage the temporal stability of input sequences with 1.52× performance speedup; 2) An attention-based context-breaking (AttenBrk) method with output speculation to reduce off-chip data accesses up to 30.3%; 3) A double-buffering scheme for CIM macros to hide writing latency and a pipeline processing element (PE) array to increase the system throughput. Measured results show that this chip achieves 6.54-to-26.03 TOPS/W energy efficiency vary from various LSTM benchmarks.\",\"PeriodicalId\":106356,\"journal\":{\"name\":\"2021 Symposium on VLSI Circuits\",\"volume\":\"1 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2021-06-13\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"4\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2021 Symposium on VLSI Circuits\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.23919/VLSICircuits52068.2021.9492492\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2021 Symposium on VLSI Circuits","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.23919/VLSICircuits52068.2021.9492492","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
A 6.54-to-26.03 TOPS/W Computing-In-Memory RNN Processor using Input Similarity Optimization and Attention-based Context-breaking with Output Speculation
This work presents a 65nm RNN processor with computing-inmemory (CIM) macros. The main contributions include: 1) A similarity analyzer (SimAyz) to fully leverage the temporal stability of input sequences with 1.52× performance speedup; 2) An attention-based context-breaking (AttenBrk) method with output speculation to reduce off-chip data accesses up to 30.3%; 3) A double-buffering scheme for CIM macros to hide writing latency and a pipeline processing element (PE) array to increase the system throughput. Measured results show that this chip achieves 6.54-to-26.03 TOPS/W energy efficiency vary from various LSTM benchmarks.
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