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

Jianfeng Wang, Zhonghao Chen, Yiming Chen, Yixin Xu, Tian Wang, Yao Yu, N. Vijaykrishnan, Sumitha George, Huazhong Yang, Xueqing Li
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引用次数: 0

摘要

随着基于nvm的深度神经网络加速器被广泛使用以提高计算效率,较长的数据保留时间本质上导致了攻击者未经授权窃取权重的高风险。权重加密是一种有效的加密方法,但现有的密文计算加速器无法实现较高的加密复杂度和灵活性。本文提出了一种基于本地解密单元(ldu)的混合粒度软硬件协同设计方法WeightLock。这项工作提出了一个键控制的单元级硬件设计,以获得更高的粒度和两种权重选择方案,以获得更高的灵活性。仿真结果表明,即使80%的密钥被泄露,VGG-8和ResNet-18在Cifar-10分类中的准确率也从80%下降到10%。与之前最先进的硬件和软件方法相比,这表明密钥泄漏容忍度提高了>20%,再训练延迟保护时间延长了>17倍。加密功能的面积成本可以忽略不计,与最先进的单元、列和1T4R结构相比,分别减少了约600x、2.2x和2.4x。
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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.
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