Sangyeob Kim, Soyeon Kim, Seongyon Hong, Sangjin Kim, Donghyeon Han, Jiwon Choi, H. Yoo
{"title":"COOL-NPU:具有CNN-SNN异构核心和事件驱动反向传播的互补在线学习神经处理单元","authors":"Sangyeob Kim, Soyeon Kim, Seongyon Hong, Sangjin Kim, Donghyeon Han, Jiwon Choi, H. Yoo","doi":"10.1109/COOLCHIPS57690.2023.10121940","DOIUrl":null,"url":null,"abstract":"This paper presents a low power NPU, COmplementary Online Learning Neural Processing Unit (COOL-NPU) with three key features: 1) low-power forward gradient generation logic with global counter and local gradient unit, 2) skip index generator and sparsity-aware CNN core for neuron-level backpropagation, 3) SNN core with distributed L1 cache to eliminate redundant SRAM access. By using complementary characteristic of CNN and SNN, we achieve 47.7% energy reduction than previous state-of-the-art online learning processor.","PeriodicalId":387793,"journal":{"name":"2023 IEEE Symposium in Low-Power and High-Speed Chips (COOL CHIPS)","volume":null,"pages":null},"PeriodicalIF":0.0000,"publicationDate":"2023-04-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"COOL-NPU: Complementary Online Learning Neural Processing Unit with CNN-SNN Heterogeneous Core and Event-driven Backpropagation\",\"authors\":\"Sangyeob Kim, Soyeon Kim, Seongyon Hong, Sangjin Kim, Donghyeon Han, Jiwon Choi, H. Yoo\",\"doi\":\"10.1109/COOLCHIPS57690.2023.10121940\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"This paper presents a low power NPU, COmplementary Online Learning Neural Processing Unit (COOL-NPU) with three key features: 1) low-power forward gradient generation logic with global counter and local gradient unit, 2) skip index generator and sparsity-aware CNN core for neuron-level backpropagation, 3) SNN core with distributed L1 cache to eliminate redundant SRAM access. By using complementary characteristic of CNN and SNN, we achieve 47.7% energy reduction than previous state-of-the-art online learning processor.\",\"PeriodicalId\":387793,\"journal\":{\"name\":\"2023 IEEE Symposium in Low-Power and High-Speed Chips (COOL CHIPS)\",\"volume\":null,\"pages\":null},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2023-04-19\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2023 IEEE Symposium in Low-Power and High-Speed Chips (COOL CHIPS)\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/COOLCHIPS57690.2023.10121940\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2023 IEEE Symposium in Low-Power and High-Speed Chips (COOL CHIPS)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/COOLCHIPS57690.2023.10121940","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
COOL-NPU: Complementary Online Learning Neural Processing Unit with CNN-SNN Heterogeneous Core and Event-driven Backpropagation
This paper presents a low power NPU, COmplementary Online Learning Neural Processing Unit (COOL-NPU) with three key features: 1) low-power forward gradient generation logic with global counter and local gradient unit, 2) skip index generator and sparsity-aware CNN core for neuron-level backpropagation, 3) SNN core with distributed L1 cache to eliminate redundant SRAM access. By using complementary characteristic of CNN and SNN, we achieve 47.7% energy reduction than previous state-of-the-art online learning processor.