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This paper investigates the energy efficiency of spiking neural networks (SNNs) by analyzing how sparsity in activity affects performance across different tasks. The authors find that while low-load feed-forward tasks can achieve significant sparsity without accuracy loss, recurrent tasks, such as language modeling, face a much higher sparsity ceiling due to the need for active states to maintain information. Their results reveal that the sparsity ceiling is fundamentally linked to the nature of the task rather than the SNN architecture, with implications for the design of neuromorphic systems.
Sparsity in spiking networks isn't a given; it heavily depends on the task, with recurrent models struggling to drop below 50% activity without sacrificing quality.
Spiking neural networks (SNNs) are promoted as an energy-efficient substrate because sparse, event-driven activity replaces dense multiply-accumulates with cheap accumulates. We argue the energy dividend of sparsity is not a property of SNNs but of the task. Holding architecture fixed and swapping only the hidden unit (continuous vs. leaky-integrate-and-fire), plus a two-sided target-firing-rate probe, we measure how far activity can be pushed down before quality breaks. Low-load feed-forward perception sparsifies to 5% firing at no accuracy cost; a recurrent language model cannot go below ~50% -- the recurrent state must stay active to carry information. A spiking Transformer, by contrast, sparsifies freely to 2% (3 seeds) -- so the ceiling is a property of recurrent compression, not sequence modeling. Attention escapes the floor only by storing the full key-value cache, trading a firing floor for a memory wall: on neuromorphic hardware, recurrence and attention pay on different axes, neither escapes. We formalize the ceiling with an information-theoretic bound rho>= H_b^{-1}(log2 M / H) and confirm its predictions: the floor rises with memory load, falls with state width, and (refuting a naive memory-only reading) rises with task difficulty. A layer-wise input floor further caps op reduction under dense input, isolating event-driven perception as where neuromorphic hardware wins.
