提出STEMS映射框架，显著降低脉冲神经网络的数据移动与能耗。
STEMS: Spatial-Temporal Mapping For Spiking Neural Networks
- 构建时空映射模型，优化层内层间数据布局以减少冗余
- 在两个视觉任务上实现12倍外存数据移动减少和5倍能效提升
- 发现部分层可无需神经元状态，使状态存储减少20倍且性能提升
脉冲神经网络（SNNs）作为类脑第三代神经网络，虽具事件驱动与稀疏计算优势，但其神经元状态随时间演化导致状态管理带来大量数据移动与存储开销，可能削弱能效优势。本文提出STE MS框架，系统探索面向真实硬件内存层次结构的SNN映射设计空间，建模神经元状态行为，优化层内与层间映射策略以最小化数据移动。实验表明，在两个基于事件的视觉SNN基准上，相比仅层内优化，可实现高达12倍的片外数据移动减少与5倍的能耗降低。进一步发现，并非所有层都需保存神经元状态；通过优化特定基准的神经元状态，实现20倍状态存储减少，同时性能提升1.4倍且无精度损失。
原文摘要 · Abstract (English)
Spiking Neural Networks (SNNs) are promising bio-inspired third-generation neural networks. Recent research has trained deep SNN models with accuracy on par with Artificial Neural Networks (ANNs). Although the event-driven and sparse nature of SNNs show potential for more energy efficient computation than ANNs, SNN neurons have internal states which evolve over time. Keeping track of SNN states can significantly increase data movement and storage requirements, potentially losing its advantages with respect to ANNs. This paper investigates the energy effects of having neuron states, and how it is influenced by the chosen mapping to realistic hardware architectures with advanced memory hierarchies. Therefore, we develop STEMS, a mapping design space exploration for SNNs. STEMS models SNN's stateful behavior and explores intra-layer and inter-layer mapping optimizations to minimize data movement, considering both spatial and temporal SNN dimensions. Using STEMS, we show up to 12x reduction in off-chip data movement and 5x reduction in energy (on top of intra-layer optimizations), on two event-based vision SNN benchmarks. Finally, neuron states may not be needed for all SNN layers. By optimizing neuron states for one of our benchmarks, we show 20x reduction in neuron states and 1.4x better performance without accuracy loss.
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