{"title":"基于12 nm 0.62-1.61 mW超低功耗数字cim的端到端始终在线视觉深度学习系统","authors":"En-Jui Chang, Cheng-Xin Xue, Chetan Deshpande, Gajanan Jedhe, Jenwei Liang, Chih-Chung Cheng, Hung-Wei Lin, Chia-Da Lee, Sushil Kumar, Kim Soon Jway, Zijie Guo, Ritesh Garg, Allen-CL Lu, Chien-Hung Lin, Meng-Han Hsieh, Tsung-Yao Lin, Chih-Cheng Chen","doi":"10.23919/VLSITechnologyandCir57934.2023.10185296","DOIUrl":null,"url":null,"abstract":"This work proposes an ultra-low power DCIM-based DL system (DCIM-DLS) for end-to-end AoV with the power range from 0.62 to 1.61 mW (INT8, 2-15 fps). Compared to the prior art [3], the power consumption of DCIM-DLS can be reduced by 70.9% based on the following techniques: 1) an area and energy efficient DCIM that reduces compute RC loading by using pushed-rule 2p8T SRAM bitcell with folded kernels selector, 2) a DCIM-friendly dataflow strategy with dual accumulators that minimizes the DCIM power of weight update and avoids redundant data movement for power saving, and 3) a reconfigurable DCIM control scheme that supports mixed-precision to further reduce power consumption.","PeriodicalId":317958,"journal":{"name":"2023 IEEE Symposium on VLSI Technology and Circuits (VLSI Technology and Circuits)","volume":"85 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2023-06-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"A 12-nm 0.62-1.61 mW Ultra-Low Power Digital CIM-based Deep-Learning System for End-to-End Always-on Vision\",\"authors\":\"En-Jui Chang, Cheng-Xin Xue, Chetan Deshpande, Gajanan Jedhe, Jenwei Liang, Chih-Chung Cheng, Hung-Wei Lin, Chia-Da Lee, Sushil Kumar, Kim Soon Jway, Zijie Guo, Ritesh Garg, Allen-CL Lu, Chien-Hung Lin, Meng-Han Hsieh, Tsung-Yao Lin, Chih-Cheng Chen\",\"doi\":\"10.23919/VLSITechnologyandCir57934.2023.10185296\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"This work proposes an ultra-low power DCIM-based DL system (DCIM-DLS) for end-to-end AoV with the power range from 0.62 to 1.61 mW (INT8, 2-15 fps). Compared to the prior art [3], the power consumption of DCIM-DLS can be reduced by 70.9% based on the following techniques: 1) an area and energy efficient DCIM that reduces compute RC loading by using pushed-rule 2p8T SRAM bitcell with folded kernels selector, 2) a DCIM-friendly dataflow strategy with dual accumulators that minimizes the DCIM power of weight update and avoids redundant data movement for power saving, and 3) a reconfigurable DCIM control scheme that supports mixed-precision to further reduce power consumption.\",\"PeriodicalId\":317958,\"journal\":{\"name\":\"2023 IEEE Symposium on VLSI Technology and Circuits (VLSI Technology and Circuits)\",\"volume\":\"85 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2023-06-11\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2023 IEEE Symposium on VLSI Technology and Circuits (VLSI Technology and Circuits)\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.23919/VLSITechnologyandCir57934.2023.10185296\",\"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 on VLSI Technology and Circuits (VLSI Technology and Circuits)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.23919/VLSITechnologyandCir57934.2023.10185296","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
A 12-nm 0.62-1.61 mW Ultra-Low Power Digital CIM-based Deep-Learning System for End-to-End Always-on Vision
This work proposes an ultra-low power DCIM-based DL system (DCIM-DLS) for end-to-end AoV with the power range from 0.62 to 1.61 mW (INT8, 2-15 fps). Compared to the prior art [3], the power consumption of DCIM-DLS can be reduced by 70.9% based on the following techniques: 1) an area and energy efficient DCIM that reduces compute RC loading by using pushed-rule 2p8T SRAM bitcell with folded kernels selector, 2) a DCIM-friendly dataflow strategy with dual accumulators that minimizes the DCIM power of weight update and avoids redundant data movement for power saving, and 3) a reconfigurable DCIM control scheme that supports mixed-precision to further reduce power consumption.
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