To maximize the performance and energy efficiency of Spiking Neural Network (SNN) processing on resource-constrained embedded systems, specialized hardware accelerators/chips are employed. However, these SNN chips may suffer from permanent faults which can affect the functionality of weight memory and neuron behavior, thereby causing potentially significant accuracy degradation and system malfunctioning. Such permanent faults may come from manufacturing defects during the fabrication process, and/or from device/transistor damages (e.g., due to wear out) during the run-time operation. However, the impact of permanent faults in SNN chips and the respective mitigation techniques have not been thoroughly investigated yet. Toward this, we propose RescueSNN, a novel methodology to mitigate permanent faults in the compute engine of SNN chips without requiring additional retraining, thereby significantly cutting down the design time and retraining costs, while maintaining the throughput and quality. The key ideas of our RescueSNN methodology are (1) analyzing the characteristics of SNN under permanent faults; (2) leveraging this analysis to improve the SNN fault-tolerance through effective fault-aware mapping (FAM); and (3) devising lightweight hardware enhancements to support FAM. Our FAM technique leverages the fault map of SNN compute engine for (i) minimizing weight corruption when mapping weight bits on the faulty memory cells, and (ii) selectively employing faulty neurons that do not cause significant accuracy degradation to maintain accuracy and throughput, while considering the SNN operations and processing dataflow. The experimental results show that our RescueSNN improves accuracy by up to 80% while maintaining the throughput reduction below 25% in high fault rate (e.g., 0.5 of the potential fault locations), as compared to running SNNs on the faulty chip without mitigation.

翻译：为在资源受限的嵌入式系统上最大化脉冲神经网络（SNN）处理的性能与能效，需采用专用硬件加速器/芯片。然而，此类SNN芯片可能因制造过程的工艺缺陷或运行期间器件/晶体管损伤（如磨损老化）引发永久性故障，从而影响权重存储器功能与神经元行为，导致精度显著下降及系统功能异常。目前，针对SNN芯片中永久性故障的影响及其缓解技术尚未得到充分研究。为此，本文提出RescueSNN——一种无需额外重训练即可缓解SNN芯片计算引擎永久性故障的创新方法，可大幅缩短设计周期与重训练成本，同时保持吞吐量与精度。RescueSNN方法论的核心思想包括：（1）分析永久故障下SNN的特性；（2）基于此分析通过高效故障感知映射（FAM）增强SNN容错能力；（3）设计轻量级硬件增强以支持FAM。FAM技术利用SNN计算引擎的故障映射图实现：①在将权重位映射至故障存储单元时最小化权重损坏；②结合SNN运算规则与处理数据流，选择性启用不会导致显著精度下降的故障神经元，以维持精度与吞吐量。实验结果表明，相比在未缓解的故障芯片上直接运行SNN，RescueSNN在高故障率（如潜在故障位置占比0.5）条件下可将精度提升最高80%，同时将吞吐量降幅控制在25%以内。