AI 中文总结
研究忆阻式人工智能硬件系统中突触元件故障问题，受星形胶质细胞启发，结合计算神经科学研究与算法恢复机制，提出基于再训练的修复机制STAR，可在监督局部学习规则下运行并扩展到卷积网络。
AI 中文摘要
忆阻交叉阵列已成为高效片上学习的有前途平台，能实现如平衡传播（EP）等局部学习规则，无需传统反向传播的内存开销。但随着设备老化，单个突触元件会积累永久性固定故障，降低模型性能，现有缓解策略有局限。本文采用受星形胶质细胞自我修复功能启发的故障恢复路径，将星形胶质细胞神经调节的计算神经科学研究与故障交叉阵列映射网络的算法恢复机制相结合。计算神经科学研究表明恢复在神经群体水平进行，据此提出基于再训练的修复机制，在监督局部学习规则下运行并可扩展到卷积网络。
英文摘要
Memristive crossbar arrays have emerged as a promising platform for efficient on-chip learning, enabling local learning rules such as Equilibrium Propagation (EP) to be realized without the memory overhead of conventional backpropagation. However, as these devices age, permanent stuck-at (SA) faults accumulate at individual synaptic elements, irreversibly corrupting the stored weights and degrading model performance in ways that in-situ retraining alone cannot address. Existing mitigation strategies either rely on hardware redundancy at significant area and power cost, or require explicit fault localization that is impractical to perform continuously on-chip. This paper adopts a brain-inspired fault recovery route motivated by self-repair functionalities enabled by astrocytes -- a type of glial cell. We couple a computational neuroscience study of astrocytic neuromodulation under permanent synaptic faults with an algorithmic recovery mechanism for faulted crossbar-mapped networks. Our computational neuroscience study characterizes how astrocytes modulate surviving synapses in a bidirectional EP-specific network setting under both SA-0 and high-conductance stuck-at fault conditions, revealing that recovery operates at the neural population level. Motivated by this observation, we propose a retraining-based repair mechanism that augments EP with an additional repair nudge anchored to pre-fault activation targets of the healthy network, encouraging surviving weights to collectively reconstruct pre-fault internal representations without any information of which weights are faulty. To our knowledge, STAR is the first astrocyte-inspired repair mechanism to operate under a supervised local learning rule and to scale to convolutional networks.
