Nowadays, the extensive exploitation of Deep Neural Networks (DNNs) in safety-critical applications raises new reliability concerns. In practice, methods for fault injection by emulation in hardware are efficient and widely used to study the resilience of DNN architectures for mitigating reliability issues already at the early design stages. However, the state-of-the-art methods for fault injection by emulation incur a spectrum of time-, design- and control-complexity problems. To overcome these issues, a novel resiliency assessment method called APPRAISER is proposed that applies functional approximation for a non-conventional purpose and employs approximate computing errors for its interest. By adopting this concept in the resiliency assessment domain, APPRAISER provides thousands of times speed-up in the assessment process, while keeping high accuracy of the analysis. In this paper, APPRAISER is validated by comparing it with state-of-the-art approaches for fault injection by emulation in FPGA. By this, the feasibility of the idea is demonstrated, and a new perspective in resiliency evaluation for DNNs is opened.

翻译：当前，深度神经网络在安全关键应用中的广泛部署引发了新的可靠性问题。实践中，通过硬件仿真进行故障注入的方法因其高效性而被广泛用于在早期设计阶段研究深度神经网络架构的韧性，以缓解可靠性问题。然而，现有基于仿真的故障注入方法存在时间、设计和控制复杂度等多方面问题。为克服这些挑战，本文提出一种名为APPRAISER的新型韧性评估方法，该方法将功能近似应用于非传统目的，并利用近似计算误差作为分析依据。通过将这一概念引入韧性评估领域，APPRAISER在保持高分析精度的同时，实现了评估过程数千倍的加速。本文通过在FPGA上与基于仿真的前沿故障注入方法进行对比验证，证明了该思想的可行性，并为深度神经网络韧性评估开辟了新视角。