[HN Gopher] Neuromorphic object localization using resistive mem...
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       Neuromorphic object localization using resistive memory, ultrasonic
       transducers
        
       Author : rntn
       Score  : 45 points
       Date   : 2022-09-28 09:34 UTC (13 hours ago)
        
 (HTM) web link (www.nature.com)
 (TXT) w3m dump (www.nature.com)
        
       | belkarx wrote:
       | Here's a TL;DR (I'm an amateur with interest in neuroscience - I
       | have no creds and this is my personal, non-specific
       | interpretation of the important parts - please feel free to
       | correct me on anything I may have misinterpreted or how this
       | explanation could be improved): There's a circuit that's fed, in
       | an event-based manner, analog, ultrasonic data from sensors (data
       | is filtered in a few ways first). The circuit has resistive
       | memory which is basically non-volatile RAM. The RRAM modules can
       | store weights, and block/allow inputs depending on its mode. The
       | circuitry also involves LIF (Leaky integrate and fire) neurons
       | which translate the amplitude of voltage into frequency (higher
       | amplitude -> higher frequency @ same amplitude). Processed waves
       | from the ultrasonic sensors are interpreted as degrees away and
       | distance (with a relatively small error margin, on quite low
       | energy compared to the same task on a microprocessor).
        
       | giantg2 wrote:
       | I'm dumb. What exactly does this mean - memory enhancement,
       | memory augmentation, just better mimicry, something else?
        
         | IHLayman wrote:
         | 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 wrote:
           | "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.
        
         | mgraczyk wrote:
         | Where are you seeing "memory enhancement" or "memory
         | augmentation"?
         | 
         | This article is about analog computation hardware, which can
         | potentially solve computer vision tasks with far less power
         | than typical hardware with digital processing.
        
           | giantg2 wrote:
           | I saw stuff about synaptic gain.
        
             | mgraczyk wrote:
             | Where? That's not in the article.
             | 
             | This is not talking about actual brains or synapses, if
             | that's what you're thinking. It's just using the same words
             | but these systems are only "inspired" by actual neurons,
             | they don't interact with neurons and don't really behave
             | like neurons except in high-level ways.
        
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       (page generated 2022-09-28 23:02 UTC)