Neuromorphic Emulation

      Vision Shouldn’t Cost Anything
    

 
      The current approach relies on massive, 
      extraordinarily expensive data centers,
      sucking up billions of gallons of water 
      just to stay cool.

      This consumes immense power, straining 
      local power grids, While this technology 
      operates at around a mere 20 Watts.
    

    Edge Computing
  

    Capable of running natively on IoT and edge 
    devices, our software doesn’t require a single 
    dedicated GPU.

    What I have here is an executable script that 
    teaches itself to learns and recognizes objects 
    as quickly and accurately as a human, while
    using no training data.
  

    Built for Efficiency
  

    Requiring virtually no power or cooling,
    it costs almost nothing to build or operate.

    With a minimal resource footprint,
    it delivers high performance
    without environmental tradeoffs.
  

    A future-proof solution; A system with 
    no emissions, and no specialized hardware.

    Unlimited scalability for organizations 
    or startups needing performance without 
    compromising cost, or efficiency.
  

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Pat.  S/N - 63/674,942

Initializing Emulator

Version:      1.37.α

Revision:     43.6y

Loading save file

Orientation-cell concatenation

Mem-network partitions

Dynamic cell-networks

Static cell-networks

Rasterizing receptive fields

Temporal frequency

Spatial-temporal integration

Image sensor to Poisson disk

Compiling shaders…

↳ Varying variable interpolation

↳ Geometry Shader

↳ Fragment shader

↳ Action potential emissive

Loading LOD engine…

Finalizing…

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    Some features may not display correctly on smaller screens.