Embedded neural network (NN) implementations are vulnerable to misclassification under fault attacks (FAs). Clock glitching and injecting strong electromagnetic (EM) pulses are two simple yet detrimental FA techniques that disrupt the NN by: 1) introducing errors in the NN model and 2) corrupting NN computation results. This article introduces the first application-specific integrated circuit (ASIC) demonstration of an energy-efficient NN accelerator equipped with built-in FA detection capabilities. We have integrated lightweight cryptography-based checks for on-chip verification to identify model errors and additionally serve as a fault detection sensor for spotting computational errors. We showcase high error-detection capabilities along with a minimal area overhead of 5.9% and negligible impact on NN accuracy.

中文翻译：

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一种高能效的神经网络加速器，具有更高的抗故障攻击能力


嵌入式神经网络 (NN) 实现在故障攻击 (FA) 下很容易出现错误分类。时钟故障和注入强电磁 (EM) 脉冲是两种简单但有害的 FA 技术，它们会通过以下方式扰乱神经网络：1) 在神经网络模型中引入错误；2) 破坏神经网络计算结果。本文介绍了第一个配备内置 FA 检测功能的节能 NN 加速器的专用集成电路 (ASIC) 演示。我们集成了基于轻量级密码学的片上验证检查，以识别模型错误，并另外充当故障检测传感器来发现计算错误。我们展示了高错误检测能力以及 5.9% 的最小面积开销，并且对神经网络精度的影响可以忽略不计。