AI Briefinghandwired: Convert JS Code to Accurate Neural Networks Without Training
chenglou/handwired
About the project
Converts general JavaScript logic into accurate neural networks without a training process. It replaces existing control structures such as if-else, loops, and state variables with matrix multiplication and ReLU operations, ensuring outputs identical to those computed by the original formulas.
Since weights are manually written, training is unnecessary, and the entire program remains differentiable. This allows single-threaded JS code to be accelerated via GPU parallel processing, or fixed logic to be combined with standard neural networks for end-to-end learning.
It uses minimal computational principles compatible with existing GPU optimization techniques, achieving millisecond-level performance in a demo of 100,000 document searches. It is released under the MIT license and can be experimentally applied in WebGPU or JS environments.
chenglou/handwired
Turn your regular JavaScript code into a neural net.
TypeScript
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