Jianfeng Wang, Zhonghao Chen, Yiming Chen, Yixin Xu, Tian Wang, Yao Yu, N. Vijaykrishnan, Sumitha George, Huazhong Yang, Xueqing Li
{"title":"在DNN加速器中使用本地解密单元进行密文计算的混合粒度权重加密方法","authors":"Jianfeng Wang, Zhonghao Chen, Yiming Chen, Yixin Xu, Tian Wang, Yao Yu, N. Vijaykrishnan, Sumitha George, Huazhong Yang, Xueqing Li","doi":"10.1109/AICAS57966.2023.10168612","DOIUrl":null,"url":null,"abstract":"With the wide use of NVM-based DNN accelerators for higher computing efficiency, the long data retention time essentially causes a high risk of unauthorized weight stealing by attackers. Weight encryption is an effective method, but existing ciphertext computing accelerators cannot achieve high encryption complexity and flexibility. This paper proposes WeightLock, a mixed-grained hardware-software co-design approach based on local decrypting units (LDUs). This work proposes a key-controlled cell-level hardware design for higher granularity and two weight selection schemes for higher flexibility. The simulation results show that the accuracy of VGG-8 and ResNet-18 in the Cifar-10 classification drops from 80% to only 10% even if 80% of keys are leaked. This shows >20% higher key leakage tolerance and >17x longer retraining latency protection, compared with the prior state-of-the-art hardware and software approaches, respectively. The area cost of the encryption function is negligible, with ~600x, 2.2x, and 2.4x reduction from the state-of-the-art cell-wise, column-wise, and 1T4R structures, respectively.","PeriodicalId":296649,"journal":{"name":"2023 IEEE 5th International Conference on Artificial Intelligence Circuits and Systems (AICAS)","volume":"44 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2023-06-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"WeightLock: A Mixed-Grained Weight Encryption Approach Using Local Decrypting Units for Ciphertext Computing in DNN Accelerators\",\"authors\":\"Jianfeng Wang, Zhonghao Chen, Yiming Chen, Yixin Xu, Tian Wang, Yao Yu, N. Vijaykrishnan, Sumitha George, Huazhong Yang, Xueqing Li\",\"doi\":\"10.1109/AICAS57966.2023.10168612\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"With the wide use of NVM-based DNN accelerators for higher computing efficiency, the long data retention time essentially causes a high risk of unauthorized weight stealing by attackers. Weight encryption is an effective method, but existing ciphertext computing accelerators cannot achieve high encryption complexity and flexibility. This paper proposes WeightLock, a mixed-grained hardware-software co-design approach based on local decrypting units (LDUs). This work proposes a key-controlled cell-level hardware design for higher granularity and two weight selection schemes for higher flexibility. The simulation results show that the accuracy of VGG-8 and ResNet-18 in the Cifar-10 classification drops from 80% to only 10% even if 80% of keys are leaked. This shows >20% higher key leakage tolerance and >17x longer retraining latency protection, compared with the prior state-of-the-art hardware and software approaches, respectively. The area cost of the encryption function is negligible, with ~600x, 2.2x, and 2.4x reduction from the state-of-the-art cell-wise, column-wise, and 1T4R structures, respectively.\",\"PeriodicalId\":296649,\"journal\":{\"name\":\"2023 IEEE 5th International Conference on Artificial Intelligence Circuits and Systems (AICAS)\",\"volume\":\"44 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 5th International Conference on Artificial Intelligence Circuits and Systems (AICAS)\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/AICAS57966.2023.10168612\",\"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 5th International Conference on Artificial Intelligence Circuits and Systems (AICAS)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/AICAS57966.2023.10168612","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
WeightLock: A Mixed-Grained Weight Encryption Approach Using Local Decrypting Units for Ciphertext Computing in DNN Accelerators
With the wide use of NVM-based DNN accelerators for higher computing efficiency, the long data retention time essentially causes a high risk of unauthorized weight stealing by attackers. Weight encryption is an effective method, but existing ciphertext computing accelerators cannot achieve high encryption complexity and flexibility. This paper proposes WeightLock, a mixed-grained hardware-software co-design approach based on local decrypting units (LDUs). This work proposes a key-controlled cell-level hardware design for higher granularity and two weight selection schemes for higher flexibility. The simulation results show that the accuracy of VGG-8 and ResNet-18 in the Cifar-10 classification drops from 80% to only 10% even if 80% of keys are leaked. This shows >20% higher key leakage tolerance and >17x longer retraining latency protection, compared with the prior state-of-the-art hardware and software approaches, respectively. The area cost of the encryption function is negligible, with ~600x, 2.2x, and 2.4x reduction from the state-of-the-art cell-wise, column-wise, and 1T4R structures, respectively.