研究忆阻器非线性更新对平衡传播训练收敛的影响。
Learning Dynamics in Memristor-Based Equilibrium Propagation
- 用六种忆阻器模型模拟非线性权重更新。
- 在分类任务中，忆阻器电阻范围至少差一个数量级时训练可稳定收敛。
- 适合关注存内计算与神经网络硬件实现的研究者。
基于忆阻器的存内计算为克服冯·诺依曼瓶颈和内存墙问题提供了新思路，可实现完全并行且能效高的向量-矩阵乘法。本文研究了忆阻器驱动的非线性权重更新对平衡传播（EqProp）训练神经网络收敛性的影响。通过电压-电流滞回特性表征六种忆阻器模型，并集成到EBANA框架中，在两个基准分类任务上进行评估。结果表明，只要忆阻器具备至少一个数量级的宽电阻范围，EqProp即可实现稳健收敛。
原文摘要 · Abstract (English)
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.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。