Memristor-based in-memory computing has emerged as a promising paradigm to overcome the constraints of the von Neumann bottleneck and the memory wall by enabling fully parallelisable and energy-efficient vector-matrix multiplications. We investigate the effect of nonlinear, memristor-driven weight updates on the convergence behaviour of neural networks trained with equilibrium propagation (EqProp). Six memristor models were characterised by their voltage-current hysteresis and integrated into the EBANA framework for evaluation on two benchmark classification tasks. EqProp can achieve robust convergence under nonlinear weight updates, provided that memristors exhibit a sufficiently wide resistance range of at least an order of magnitude.

翻译：基于忆阻器的存内计算已成为一种突破冯·诺依曼瓶颈与内存墙限制的前沿范式，通过实现完全可并行化且高能效的向量-矩阵乘法。本研究探究了非线性忆阻器驱动权重更新对采用平衡传播（EqProp）训练的神经网络收敛行为的影响。通过电压-电流迟滞特性表征了六种忆阻器模型，并将其集成至EBANA框架中，在两个基准分类任务上进行评估。实验表明：当忆阻器具备至少一个数量级的足够宽阻值范围时，平衡传播算法能够在非线性权重更新条件下实现鲁棒收敛。