This survey paper presents a comprehensive examination of Spiking Neural Network (SNN) architecture search (SNNaS) from a unique hardware/software co-design perspective. SNNs, inspired by biological neurons, have emerged as a promising approach to neuromorphic computing. They offer significant advantages in terms of power efficiency and real-time resource-constrained processing, making them ideal for edge computing and IoT applications. However, designing optimal SNN architectures poses significant challenges, due to their inherent complexity (e.g., with respect to training) and the interplay between hardware constraints and SNN models. We begin by providing an overview of SNNs, emphasizing their operational principles and key distinctions from traditional artificial neural networks (ANNs). We then provide a brief overview of the state of the art in NAS for ANNs, highlighting the challenges of directly applying these approaches to SNNs. We then survey the state of the art in SNN-specific NAS approaches. Finally, we conclude with insights into future research directions for SNN research, emphasizing the potential of hardware/software co-design in unlocking the full capabilities of SNNs. This survey aims to serve as a valuable resource for researchers and practitioners in the field, offering a holistic view of SNNaS and underscoring the importance of a co-design approach to harness the true potential of neuromorphic computing.

翻译：本综述论文从独特的硬件/软件协同设计视角，对脉冲神经网络架构搜索进行了全面审视。受生物神经元启发的脉冲神经网络已成为神经形态计算领域一种前景广阔的方法。它们在能效和实时资源受限处理方面具有显著优势，使其成为边缘计算和物联网应用的理想选择。然而，设计最优的脉冲神经网络架构面临重大挑战，这源于其固有的复杂性（例如在训练方面）以及硬件约束与脉冲神经网络模型之间的相互作用。我们首先概述脉冲神经网络，重点阐述其运行原理以及与传统人工神经网络的关键区别。随后简要回顾人工神经网络架构搜索的最新进展，强调将这些方法直接应用于脉冲神经网络所面临的挑战。接着系统梳理脉冲神经网络专用架构搜索方法的研究现状。最后，我们展望脉冲神经网络研究的未来方向，重点探讨硬件/软件协同设计在释放脉冲神经网络全部潜能方面的前景。本综述旨在为该领域的研究人员和实践者提供有价值的参考，通过呈现脉冲神经网络架构搜索的整体图景，强调采用协同设计方法对发挥神经形态计算真正潜力的重要性。