  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.
