Spiking Neural Networks (SNNs) are inspired by the sparse and event-driven nature of biological neural processing, and offer the potential for ultra-low-power artificial intelligence. However, realizing their efficiency benefits requires specialized hardware and a co-design approach that effectively leverages sparsity. We explore the hardware-software co-design of sparse SNNs, examining how sparsity representation, hardware architectures, and training techniques influence hardware efficiency. We analyze the impact of static and dynamic sparsity, discuss the implications of different neuron models and encoding schemes, and investigate the need for adaptability in hardware designs. Our work aims to illuminate the path towards embedded neuromorphic systems that fully exploit the computational advantages of sparse SNNs.

翻译：脉冲神经网络（SNNs）受生物神经处理的稀疏性与事件驱动特性启发，为实现超低功耗人工智能提供了潜力。然而，要充分发挥其能效优势，需要专门的硬件以及能有效利用稀疏性的协同设计方法。本文探讨了稀疏SNN的软硬件协同设计，分析了稀疏性表示、硬件架构以及训练技术如何影响硬件效率。我们研究了静态与动态稀疏性的影响，讨论了不同神经元模型与编码方案的意义，并探究了硬件设计中对适应性的需求。本工作旨在阐明一条发展路径，以推动嵌入式神经形态系统充分利用稀疏SNN的计算优势。