Spiking Neural Network (SNN) With Memristor Synapses Having Non-linear Weight Update - Kim, Taeyoon; Hu, Suman; Kim, Jaewook; Kwak, Joon Young; Park, Jongkil; Lee, Suyoun; Kim, Inho; Park, Jong-Keuk; Jeong, YeonJoo (Frontiers in Computational Neuroscience, 2021)
Among many artificial neural networks, the research on Spike Neural Network (SNN), which mimics the energy-efficient signal system in the brain, is drawing much attention. Memristor is a promising candidate as a synaptic component for hardware implementation of SNN, but several non-ideal device properties are making it challengeable. In this work, we conducted an SNN simulation by adding a device model with a non-linear weight update to test the impact on SNN performance. We found that SNN has a strong tolerance for the device non-linearity and the network can keep the accuracy high if a device meets one of the two conditions: 1. symmetric long-term potentiation (LTP) and long-term depression (LTD) curves and 2. positive non-linearity factors for both LTP and LTD. The reason was analyzed in terms of the balance between network parameters as well as the variability of weight. The results are considered to be a piece of useful prior information for the future implementation of emerging device-based neuromorphic hardware.