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…