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- Title
Neuroevolution Guided Hybrid Spiking Neural Network Training.
- Authors
Lu, Sen; Sengupta, Abhronil
- Abstract
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.
- Subjects
DISRUPTIVE innovations; MACHINE learning
- Publication
Frontiers in Neuroscience, 2022, p1
- ISSN
1662-4548
- Publication type
Article
- DOI
10.3389/fnins.2022.838523