[IHLayman]

I will take a stab at this... it is about object localization via sound instead of sight, using very little power. For comparison, let's say you have a sound sensing setup on a Raspberry Pi, known for its low power draw, attached to a pair of microphones, looking to alert the location of an object by similar sound triangulations. That processing could take maybe 4-6W of power (continuous running of the Pi and attached mics, as a generous estimate), and could be quite effective.

However the technique in this paper is _ultra_ low power. First off, they model the design off of a barn owl, and using "neuromorphic memristors" (sounds technobabble to me but I didn't understand that part). But in the Results part of the paper they claim they can sense movement sampling every 1/10th of a second using only 250 microwatts of power, orders of magnitude more efficient than a naive approach, with only 22 floating-point calcluations per sample. Sounds quite impressive, but I wonder what the actual applications of this will be, even though I'd love to be able to track mice around my house in real time grrrr.

[elcritch]

"neuromorphic memristors" let you build neural-nets in hardware. Memristor act as a sort of variable resistor based on how much current has flowed through them. The weights in neural networks can be stored as the memory effect in each memristor.

A few years back HP was researching memristors to produce neural net processors, but I never heard of anything coming from it.

It's a pretty clever way to deploy a neural net algorithm using very low power. Maybe HP was looking at the wrong market.