Rate-encoded SNNs with T timesteps outperform bit-equivalent QNNs in energy only when average spike rate falls below 6.4% for T in [5,10] under typical neuromorphic hardware, per an analytical model covering computation and data movement.
A method to estimate the energy consumption of deep neural networks
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Reconsidering the energy efficiency of spiking neural networks
Rate-encoded SNNs with T timesteps outperform bit-equivalent QNNs in energy only when average spike rate falls below 6.4% for T in [5,10] under typical neuromorphic hardware, per an analytical model covering computation and data movement.