物理非克隆内存计算,同时保护私人数据和深度学习模型.
Wenshuo Yue1,2, Kai Wu3, Zhiyuan Li1
1Beijing Advanced Innovation Center for Integrated Circuits, School of Integrated Circuits, Peking University, Beijing, China.
Nature communications
|January 25, 2025
概括
电阻随机访问内存计算内存提供了高效的神经网络加速,但有风险的数据提取. RePACK 方案通过保护输入,重量和结构来增强安全性,从而提高了清单复杂性,以获得更安全的边缘AI.
科学领域:
- 计算机工程 计算机工程
- 硬件安全 硬件安全
- 人工智能的人工智能
背景情况:
- 使用电阻随机访问存储器 (ReRAM) 的内存计算 (CIM) 加快边缘设备上的神经网络,提高能源效率.
- ReRAM的非易失性质带来安全风险,允许在计算过程中潜在地提取敏感的神经网络重量.
研究的目的:
- 提出一个强大的数据保护方案,RePACK,用于基于ReRAM的CIM系统中安全的神经网络计算.
- 保护神经网络输入数据,存储的权重和结构信息免受未经授权的提取.
主要方法:
- 开发了RePACK,这是一个三重数据保护方案,集成了双方排序编码策略.
- 整合了完全在芯片上的物理不可克隆功能 (PUF) 以提高安全性.
- 在40nm的ReRAM CIM芯片上实现并评估了RePACK系统.
主要成果:
- 成功保护神经网络输入,重量和结构信息.
- 在128列CIM核心中,编号复杂度显著增加到5.77 × 10^75.
- 在物理硬件原型上验证了RePACK系统的有效性.
结论:
- RePACK提供了一种可行的解决方案,用于增强边缘神经网络的基于ReRAM的CIM系统的安全性.
- 这项工作有助于开发安全,强大和高效的边缘AI加速器,可能支持诸如联合学习之类的应用程序.
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