  Spiking neural networks (SNNs) have shown promise in various dynamic visual
tasks, yet those ready for practical deployment often lack the compactness and
robustness essential in resource-limited and safety-critical settings. Prior
research has predominantly concentrated on enhancing the compactness or
robustness of artificial neural networks through strategies like network
pruning and adversarial training, with little exploration into similar
methodologies for SNNs. Robust pruning of SNNs aims to reduce computational
overhead while preserving both accuracy and robustness. Current robust pruning
approaches generally necessitate expert knowledge and iterative experimentation
to establish suitable pruning criteria or auxiliary modules, thus constraining
their broader application. Concurrently, evolutionary algorithms (EAs) have
been employed to automate the pruning of artificial neural networks, delivering
remarkable outcomes yet overlooking the aspect of robustness. In this work, we
propose CCSRP, an innovative robust pruning method for SNNs, underpinned by
cooperative co-evolution. Robust pruning is articulated as a tri-objective
optimization challenge, striving to balance accuracy, robustness, and
compactness concurrently, resolved through a cooperative co-evolutionary
pruning framework that independently prunes filters across layers using EAs.
Our experiments on CIFAR-10 and SVHN demonstrate that CCSRP can match or exceed
the performance of the latest methodologies.
