Kodai Ueyoshi, Kota Ando, Kazutoshi Hirose, Shinya Takamaeda-Yamazaki, J. Kadomoto, T. Miyata, M. Hamada, T. Kuroda, M. Motomura
{"title":"QUEST:一个7.49TOPS多用途对数量化DNN推理引擎,采用40nm CMOS电感耦合技术,堆叠在96MB 3D SRAM上","authors":"Kodai Ueyoshi, Kota Ando, Kazutoshi Hirose, Shinya Takamaeda-Yamazaki, J. Kadomoto, T. Miyata, M. Hamada, T. Kuroda, M. Motomura","doi":"10.1109/ISSCC.2018.8310261","DOIUrl":null,"url":null,"abstract":"A key consideration for deep neural network (DNN) inference accelerators is the need for large and high-bandwidth external memories. Although an architectural concept for stacking a DNN accelerator with DRAMs has been proposed previously, long DRAM latency remains problematic and limits the performance [1]. Recent algorithm-level optimizations, such as network pruning and compression, have shown success in reducing the DNN memory size [2]; however, since networks become irregular and sparse, they induce an additional need for agile random accesses to the memory systems.","PeriodicalId":6617,"journal":{"name":"2018 IEEE International Solid - State Circuits Conference - (ISSCC)","volume":"135 1","pages":"216-218"},"PeriodicalIF":0.0000,"publicationDate":"2018-03-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"62","resultStr":"{\"title\":\"QUEST: A 7.49TOPS multi-purpose log-quantized DNN inference engine stacked on 96MB 3D SRAM using inductive-coupling technology in 40nm CMOS\",\"authors\":\"Kodai Ueyoshi, Kota Ando, Kazutoshi Hirose, Shinya Takamaeda-Yamazaki, J. Kadomoto, T. Miyata, M. Hamada, T. Kuroda, M. Motomura\",\"doi\":\"10.1109/ISSCC.2018.8310261\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"A key consideration for deep neural network (DNN) inference accelerators is the need for large and high-bandwidth external memories. Although an architectural concept for stacking a DNN accelerator with DRAMs has been proposed previously, long DRAM latency remains problematic and limits the performance [1]. Recent algorithm-level optimizations, such as network pruning and compression, have shown success in reducing the DNN memory size [2]; however, since networks become irregular and sparse, they induce an additional need for agile random accesses to the memory systems.\",\"PeriodicalId\":6617,\"journal\":{\"name\":\"2018 IEEE International Solid - State Circuits Conference - (ISSCC)\",\"volume\":\"135 1\",\"pages\":\"216-218\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2018-03-08\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"62\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2018 IEEE International Solid - State Circuits Conference - (ISSCC)\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/ISSCC.2018.8310261\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2018 IEEE International Solid - State Circuits Conference - (ISSCC)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/ISSCC.2018.8310261","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
QUEST: A 7.49TOPS multi-purpose log-quantized DNN inference engine stacked on 96MB 3D SRAM using inductive-coupling technology in 40nm CMOS
A key consideration for deep neural network (DNN) inference accelerators is the need for large and high-bandwidth external memories. Although an architectural concept for stacking a DNN accelerator with DRAMs has been proposed previously, long DRAM latency remains problematic and limits the performance [1]. Recent algorithm-level optimizations, such as network pruning and compression, have shown success in reducing the DNN memory size [2]; however, since networks become irregular and sparse, they induce an additional need for agile random accesses to the memory systems.