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System And Method For Parameter Multiplexed Gradient Descent
Published Patent Application Number: 2024/0028879
Problem
The existing training method, “backpropagation,” is difficult to implement in hardware and becomes untenable when the hardware is imperfect.
Invention
Parameter multiplexed gradient descent (PMGD) is a machine learning training method designed to easily train emergent non-volatile memory and neuromorphic hardware platforms.
Potential Commercial Applications
Online training of both analog and digital systems. Useful for in-the-field training of custom machine learning and neuromorphic hardware.
Competitive Advantage
Hardware networks can be trained much faster and use less energy than current equivalent techniques. It is also robust to hardware imperfections and noise.