🤖 AI Summary
Spike Neural Network Architecture Search (SNNaS) faces fundamental challenges including high training complexity and difficulties in co-modeling software and hardware. This paper presents the first systematic survey of recent SNNaS advances from a hardware–software co-design perspective. It analyzes the limitations of conventional Neural Architecture Search (NAS) methods when applied to SNNs, and reveals intrinsic differences between SNNs and Artificial Neural Networks (ANNs) in training dynamics, temporal behavior, and hardware constraints—such as latency, power consumption, and memory bandwidth. To address these, we integrate spike dynamics modeling, differentiable and reinforcement-based NAS algorithms, and hardware performance evaluation frameworks, proposing a constraint-aware search paradigm tailored for edge computing and IoT applications. Our work establishes a unified analytical framework and practical guidelines for SNNaS, advancing the automated design of low-power, high-energy-efficiency neuromorphic computing systems.
📝 Abstract
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.