Hyperparameter optimization (HPO) is of paramount importance in the development of high-performance, specialized artificial intelligence (AI) models, ranging from well-established machine learning (ML) solutions to the deep learning (DL) domain and the field of spiking neural networks (SNNs). The latter introduce further complexity due to the neuronal computational units and their additional hyperparameters, whose inadequate setting can dramatically impact the final model performance. At the cost of possible reduced generalization capabilities, the most suitable strategy to fully disclose the power of SNNs is to adopt an application-oriented approach and perform extensive HPO experiments. To facilitate these operations, automatic pipelines are fundamental, and their configuration is crucial. In this document, the Neural Network Intelligence (NNI) toolkit is used as reference framework to present one such solution, with a use case example providing evidence of the corresponding results. In addition, a summary of published works employing the presented pipeline is reported as a potential source of insights into application-oriented HPO experiments for SNN prototyping.

翻译：超参数优化（HPO）对于开发高性能、专用人工智能（AI）模型至关重要，其应用范围涵盖成熟的机器学习（ML）解决方案、深度学习（DL）领域以及脉冲神经网络（SNN）领域。后者由于神经元计算单元及其额外的超参数而引入了进一步的复杂性，这些参数设置不当会显著影响最终模型性能。尽管可能降低泛化能力，但充分释放SNN潜力的最合适策略是采用面向应用的方法并进行广泛的HPO实验。为便于这些操作，自动化流程至关重要，其配置尤为关键。本文以神经网络智能（NNI）工具包为参考框架，介绍了这样一种解决方案，并通过用例示例展示了相应结果。此外，本文还汇总了采用该流程的已发表工作，为面向应用的SNN原型HPO实验提供了潜在的见解来源。