Simon Richter

Sparse, quantized linear-attention models can outperform dense counterparts on neuromorphic hardware, achieving up to 37× higher throughput and 16× lower power consumption.

Allocating synaptic weights based on neuron firing rates can slash power consumption by over 60% in Spiking Neural Networks without significant area costs.