Neuromorphic, or spiking, processors are increasingly being considered for use in harsh, radiation-prone environments such as space and avionics, where energy efficiency and graceful degradation are essential. In this study, we propose and experimentally validate a radiation-testing methodology specifically designed for neuromorphic processors that employ on-chip synaptic plasticity. We map the open-source ODIN SNN processor with Spike-Dependent Synaptic Plasticity (SDSP) onto the FPGA and expose it to a high-energy neutron beam while continuously monitoring MNIST classification accuracy and recording the synaptic state. From these measurements, we extract SEU cross-sections for ODIN's synaptic memory and develop a calibrated fault model to inform a complementary fault-injection campaign. By comparing inference-only and online-learning configurations, we demonstrate that enabling SDSP can significantly extend the time to application-level failure and enable partial recovery from accumulated bit flips, with modest hardware overhead.

翻译：神经形态（即脉冲）处理器正越来越多地被考虑用于太空和航空电子等严峻、高辐射环境，这些环境对能效和优雅降级至关重要。本研究提出并实验验证了一种专门针对采用片上突触可塑性的神经形态处理器的辐射测试方法。我们将具有突触可塑性依赖脉冲（SDSP）的开源ODIN SNN处理器映射到FPGA上，并使其暴露于高能中子束中，同时持续监测MNIST分类精度并记录突触状态。通过这些测量，我们提取了ODIN突触存储器的单粒子翻转（SEU）截面，并开发了一个校准的故障模型以支持补充性的故障注入实验。通过比较仅推理和在线学习配置，我们证明启用SDSP可以显著延长应用级故障发生时间，并实现累积比特翻转的部分恢复，同时硬件开销适中。