SpikeFI：面向脉冲神经网络的故障注入框架
SpikeFI: A Fault Injection Framework for Spiking Neural Networks
AI总结：
本文提出开源故障注入框架SpikeFI，基于SLAYER PyTorch并支持GPU加速，提供可扩展的神经元与突触故障模型及多种注入模式，用于自动化评估SNN在硬件故障下的可靠性。
摘要：
神经形态计算和脉冲神经网络（SNN）凭借其在能源高效利用和更快计算速度方面的潜力，正越来越多地应用于各类人工智能（AI）任务。这一比较优势源于对生物大脑结构、功能和效率的模仿；生物大脑可以说是最出色、最绿色的计算机器。由于SNN最终会部署在硬件处理器上，面对硬件级故障时应用的可靠性便成为一个值得关注的问题，尤其是在安全关键和任务关键型应用中。在本研究中，我们提出了SpikeFI，这是一个用于SNN的故障注入框架，可用于自动化可靠性分析和测试生成。SpikeFI构建于SLAYER PyTorch框架之上，故障注入实验可在单个或多个GPU上加速。它拥有一个全面集成的神经元与突触故障模型库，符合该领域的相关文献，并可在需要时由用户扩展。该框架支持：单故障和多故障；永久故障和瞬时故障；指定位置、逐层随机位置和全网随机位置的故障位置；以及训练前、训练中和训练后的故障注入。它还提供若干优化加速手段和用于结果可视化的内置函数。SpikeFI是开源的，可通过GitHub下载：https://github.com/SpikeFI。
英文摘要：
Neuromorphic computing and spiking neural networks (SNNs) are gaining traction across various artificial intelligence (AI) tasks thanks to their potential for efficient energy usage and faster computation speed. This comparative advantage comes from mimicking the structure, function, and efficiency of the biological brain, which arguably is the most brilliant and green computing machine. As SNNs are eventually deployed on a hardware processor, the reliability of the application in light of hardware-level faults becomes a concern, especially for safety- and mission-critical applications. In this work, we propose SpikeFI, a fault injection framework for SNNs that can be used for automating the reliability analysis and test generation. SpikeFI is built upon the SLAYER PyTorch framework with fault injection experiments accelerated on a single or multiple GPUs. It has a comprehensive integrated neuron and synapse fault model library, in accordance to the literature in the domain, which is extendable by the user if needed. It supports: single and multiple faults; permanent and transient faults; specified, random layer-wise, and random network-wise fault locations; and pre-, during, and post-training fault injection. It also offers several optimization speedups and built-in functions for results visualization. SpikeFI is open-source and available for download via GitHub at https://github.com/SpikeFI.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
