Neuroevolution Guided Hybrid Spiking Neural Network Training - Lu, Sen; Sengupta, Abhronil (Frontiers in Neuroscience, 2022)
Neuromorphic computing algorithms based on Spiking Neural Networks (SNNs) are evolving to be a disruptive technology driving machine learning research. The overarching goal of this work is to develop a structured algorithmic framework for SNN training that optimizes unique SNN-specific properties like neuron spiking threshold using neuroevolution as a feedback strategy. We provide extensive results for this hybrid bio-inspired training strategy and show that such a feedback-based learning approach leads to explainable neuromorphic systems that adapt to the specific underlying application. Our analysis reveals 53.8%, 28.8% and 28.2% latency improvement for the neuroevolution-based SNN training strategy on CIFAR-10, CIFAR-100 and ImageNet datasets respectively in contrast to state-of-the-art conversion based approaches. The proposed algorithm can be easily extended to other application domains like image classification in presence of adversarial attacks where 43.2% and 27.9% latency improvements were observed on CIFAR-10 and CIFAR-100 datasets respectively.