H2Learn: High-Efficiency Learning Accelerator for High-Accuracy Spiking Neural Networks

Understand

Although spiking neural networks (SNNs) take benefits from the bio-plausible neural modeling, the low accuracy under the common local synaptic plasticity learning rules limits their application in many practical tasks.

• Recently, an emerging SNN supervised learning algorithm inspired by backpropagation through time (BPTT) from the domain of artificial neural networks (ANNs) has successfully boosted the accuracy of SNNs and helped improve the practicability of SNNs.
• However, current general-purpose processors suffer from low efficiency when performing BPTT for SNNs due to the ANN-tailored optimization.
• On the other hand, current neuromorphic chips cannot support BPTT because they mainly adopt local synaptic plasticity rules for simplified implementation.