07/15/2026
Most neuromorphic projects give the advantage back before they ship.
They adopt a sparse architecture, then feed it rate coding, where a big number means a long dense burst of spikes. Sparse hardware, dense workload. You can watch the efficiency leak out.
We encode differently. Time to first spike. The value lives in when the neuron fires, not how often. One spike carries the number and the work is done.
The measured result: 304 spikes per inference where a rate coded equivalent burns 15,847. A 52x reduction. Every spike you remove is a synaptic operation you never run and a joule you never spend.
That is the entire reason neuromorphic inference works at the edge, and it is verified across 78.1 million network flows through the George Washington University Doctor of Engineering program.
No spike, no operation, no energy spent.
https://www.rfr.bz/f99a392bb