Neuromorphic Emulation

      The current means of computer vision 
      requires massive data centers,
    

 
      Filled with thousands of GPUs requiring billions of gallons 
      of water to cool, and billions of watts to power, straining 
      the power grid and water pressure of the surrounding arias 
      around these massive data centers. 
      
      This technology need none of the above.
    

    Capable of running natively on something 
    as little as edge, or handheld IoT devices,
  

    Not requiring even a single dedicated GPU to learn and 
    recognize objects faster than humans, while drastically 
    out preforming current means of computer vision.
    
    No labels. No training data. Just a single executable; 
    A script that teaches itself to see entirely on its own, 
    learning to interpret and understand images faster than 
    humans, just as well as humans.
  

    Requiring virtually no power or cooling 
    and costs nothing to build or operate,
  

    This software stands as the most cost-effective, highest-
    performing, and environmentally friendly solution possible. 
    Its minimal resource footprint not only reduces operational 
    overhead but also aligns with global sustainability goals. 

    With zero emissions, no hardware dependencies, and 
    unmatched scalability, this system offers a future -
    proof solution for organizations seeking performance 
    without compromise - on budget, efficiency, or the planet.
  

    Whether you're a startup looking for sustainable solutions,
    or an enterprise modernizing legacy systems, this solution
    delivers consistent, top-notch performance, combined 
    with high sustainability at scale.


    This is more than just a software solution, 
     it’s a catalyst for change.
  

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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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