As artificial neural networks become increasingly integrated into safety-critical systems such as autonomous vehicles, devices for medical diagnosis, and industrial automation, ensuring their reliability in the face of random hardware faults becomes paramount. This paper introduces SpikingJET, a novel fault injector designed specifically for fully connected and convolutional Spiking Neural Networks (SNNs). Our work underscores the critical need to evaluate the resilience of SNNs to hardware faults, considering their growing prominence in real-world applications. SpikingJET provides a comprehensive platform for assessing the resilience of SNNs by inducing errors and injecting faults into critical components such as synaptic weights, neuron model parameters, internal states, and activation functions. This paper demonstrates the effectiveness of Spiking-JET through extensive software-level experiments on various SNN architectures, revealing insights into their vulnerability and resilience to hardware faults. Moreover, highlighting the importance of fault resilience in SNNs contributes to the ongoing effort to enhance the reliability and safety of Neural Network (NN)-powered systems in diverse domains.

翻译：随着人工神经网络日益集成到自动驾驶、医疗诊断设备和工业自动化等安全关键系统中，确保其在面对随机硬件故障时的可靠性变得至关重要。本文提出SpikingJET——一种专为全连接和卷积脉冲神经网络设计的创新型故障注入工具。针对脉冲神经网络在现实应用中日益凸显的重要性，本工作强调了评估其对硬件故障弹性的关键需求。SpikingJET通过向突触权重、神经元模型参数、内部状态及激活函数等关键组件注入错误与故障，提供了评估脉冲神经网络弹性的综合平台。本文通过对多种脉冲神经网络架构进行广泛的软件级实验，验证了SpikingJET的有效性，揭示了其在硬件故障下的脆弱性与弹性特征。此外，突出脉冲神经网络故障弹性的重要性，将推动不同领域中以神经网络为驱动的系统在可靠性与安全性方面的持续改进。