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模拟内存计算的固有的对抗性稳定性

Corey Lammie¹, Julian Büchel², Athanasios Vasilopoulos²

¹IBM Research Europe, Rüschlikon, Switzerland. corey.lammie@ibm.com.

Nature communications

|February 19, 2025

概括

模拟内存计算芯片增强了对抗攻击的深度神经网络稳定性. 这项研究通过实验验证,这些系统中固有的噪音为图像分类和自然语言处理任务提供了重要的防御.

科学领域:

计算机科学计算机科学
电气工程电气工程
材料科学材料科学材料科学

背景情况:

深度神经网络 (DNN) 容易受到对抗性攻击,造成安全风险.
模拟内存计算 (AIMC) 基板,利用像相变内存 (PCM) 这样的设备,理论上可以提供固有的对抗性强度.
缺乏对AIMC对DNN推断的对抗性稳定性的实验验证.

研究的目的:

在AIMC芯片上实验验验证DNN推理的对抗性稳定性.
调查噪声对AIMC系统对抗性强度的来源和影响.
在AIMC上评估较大的变压器网络的稳定性,用于自然语言处理 (NLP) 任务.

主要方法:

在基于PCM的AIMC芯片上实现图像分类DNN.
对各种攻击类型的对抗性强度的评估,包括硬件在循环中的 (HITL) 攻击.
对有助于强度的随机噪声源 (经常性和非经常性) 的分析.
在AIMC上模拟用于NLP任务的变压器网络.

主要成果:

与图像分类的传统方法相比,AIMC芯片显示出明显更高的对抗性稳定性.
此外,HITL攻击的有效性也降低了,这表明了硬件级防御.
随机噪声来源被确定为对抗性强度的主要贡献者,其特征显著影响防御.
模拟证实在NLP任务中更大的变压器网络的持续稳定性.

结论:

模拟内存计算,特别是基于PCM的系统,为增强DNN对抗性强度提供了一个有希望的基于硬件的解决方案.
在AIMC中固有的随机噪声对这种防御机制至关重要.
在不同的网络架构和任务中,AIMC是强大的AI推断的可行基质.

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