Comment by HarHarVeryFunny

The energy inefficiency of ANNs vs our brain is mostly because our brain operates in async dataflow mode with each neuron mostly consuming energy only when it fires. If a neuron's inputs haven't changed then it doesn't redundantly "recalculate it's output" like an ANN - it just does nothing.

You could certainly implement an async dataflow type design in software, although maybe not as power efficiently as with custom silicon, but individual ANN node throughput performance would suffer given the need to aggregate neurons needing updates into a group to be fed into one the large matrix multiplies that today's hardware is optimized for, although sparse operations are also a possibility. OTOH conceivably one could save enough FLOPs that it'd still be a win in terms of how fast an input could be processed through an entire neural net.