Spiking Neural Networks (SNNs) have gained attention for their energy-efficient machine learning capabilities, utilizing bio-inspired activation functions and sparse binary spike-data representations. While recent SNN algorithmic advances achieve high accuracy on large-scale computer vision tasks, their energy-efficiency claims rely on certain impractical estimation metrics. This work studies two hardware benchmarking platforms for large-scale SNN inference, namely SATA and SpikeSim. SATA is a sparsity-aware systolic-array accelerator, while SpikeSim evaluates SNNs implemented on In-Memory Computing (IMC) based analog crossbars. Using these tools, we find that the actual energy-efficiency improvements of recent SNN algorithmic works differ significantly from their estimated values due to various hardware bottlenecks. We identify and address key roadblocks to efficient SNN deployment on hardware, including repeated computations & data movements over timesteps, neuronal module overhead, and vulnerability of SNNs towards crossbar non-idealities.

翻译：脉冲神经网络（SNN）因其利用仿生激活函数和稀疏二进制脉冲数据表征的节能机器学习能力而备受关注。尽管近期SNN算法在大规模计算机视觉任务上取得了高精度，但其节能性声称依赖于某些不切实际的评估指标。本研究分析了两种用于大规模SNN推理的硬件基准测试平台——SATA与SpikeSim。SATA是一种稀疏感知脉动阵列加速器，而SpikeSim评估基于存内计算（IMC）的模拟交叉开关上实现的SNN。利用这些工具，我们发现近期SNN算法工作的实际能效提升因各类硬件瓶颈而显著偏离其估计值。我们识别并解决了SNN在硬件上高效部署的关键障碍，包括时间步长上的重复计算与数据移动、神经元模块开销，以及SNN对交叉开关非理想特性的脆弱性。