Memristor-Spikelearn: A Spiking Neural Network Simulator for Studying Synaptic Plasticity under Realistic Device and Circuit Behaviors

We present the Memristor-Spikelearn simulator (open-sourced), which is capable of incorporating detailed mem-ristor and circuit models in simulation to enable thorough study of synaptic plasticity in spiking neural networks under realistic device and circuit behaviors. Using this simulator, we demonstrate that: (1) a detailed device model is essential for simulating synaptic plasticity workloads, because results obtained using a simplified model can be misleading (e.g., it can overestimate test accuracy by up to 21.9%); (2) detailed simulation helps to determine the proper range of conductance values to represent weights, which is critical in order to achieve the desired accuracy -energy tradeoff (e.g., increasing the conductance values by
$10\times$
can increase accuracy from 70% to 83% at the price of
$20\times$
higher energy); and (3) detailed simulation also helps to determine an optimized circuit structure, which is another important design parameter that can yield different accuracy -energy tradeoffs.