论文标题
通过安全的重量映射增强Memristor计算系统的安全性
Enhancing Security of Memristor Computing System Through Secure Weight Mapping
论文作者
论文摘要
新兴的Memristor计算系统在提高神经网络（NN）算法的能源效率方面表现出了巨大的希望。然而，存储在Memristor横杆中的NN权重可能会因备忘录设备的不易作用而面临潜在的盗窃攻击。在本文中，我们建议通过以1的补充形式绘制其选定的列来保护NN权重，并以其原始形式的其他列将其他列留下，从而阻止对手知道每个权重的确切表示。结果表明，与先前的工作相比，我们的方法实现了与最好的方法相当的有效性，并将硬件开销降低了18倍以上。
Emerging memristor computing systems have demonstrated great promise in improving the energy efficiency of neural network (NN) algorithms. The NN weights stored in memristor crossbars, however, may face potential theft attacks due to the nonvolatility of the memristor devices. In this paper, we propose to protect the NN weights by mapping selected columns of them in the form of 1's complements and leaving the other columns in their original form, preventing the adversary from knowing the exact representation of each weight. The results show that compared with prior work, our method achieves effectiveness comparable to the best of them and reduces the hardware overhead by more than 18X.
