Spiking neural networks (SNNs) have gained prominence for their potential in
neuromorphic computing and energy-efficient artificial intelligence, yet
optimizing them remains a formidable challenge for gradient-based methods due
to their discrete, spike-based computation. This paper attempts to tackle the
challenges by introducing Cosine Annealing Differential Evolution (CADE),
designed to modulate the mutation factor (F) and crossover rate (CR) of
differential evolution (DE) for the SNN model, i.e., Spiking Element Wise (SEW)
ResNet. Extensive empirical evaluations were conducted to analyze CADE. CADE
showed a balance in exploring and exploiting the search space, resulting in
accelerated convergence and improved accuracy compared to existing
gradient-based and DE-based methods. Moreover, an initialization method based
on a transfer learning setting was developed, pretraining on a source dataset
(i.e., CIFAR-10) and fine-tuning the target dataset (i.e., CIFAR-100), to
improve population diversity. It was found to further enhance CADE for SNN.
Remarkably, CADE elevates the performance of the highest accuracy SEW model by
an additional 0.52 percentage points, underscoring its effectiveness in
fine-tuning and enhancing SNNs. These findings emphasize the pivotal role of a
scheduler for F and CR adjustment, especially for DE-based SNN. Source Code on
Github: this https URL

通过引入余弦退火差分进化（CADE）方法，本论文尝试解决脉冲神经网络（SNNs）优化的挑战，通过调整差分进化（DE）的变异因子和交叉率来获得更好的收敛和准确性。使用基于迁移学习的初始化方法对 SNN 进行改进，进一步提高了 CADE 的性能。研究结果表明，F 和 CR 调整的调度器对于基于 DE 的 SNN 尤为重要。