Machine Learning-powered Compact Modeling of Stochastic Electronic Devices using Mixture Density Networks

no code implementations • 10 Nov 2023 • Jack Hutchins, Shamiul Alam, Dana S. Rampini, Bakhrom G. Oripov, Adam N. McCaughan, Ahmedullah Aziz

The relentless pursuit of miniaturization and performance enhancement in electronic devices has led to a fundamental challenge in the field of circuit design and simulation: how to accurately account for the inherent stochastic nature of certain devices.

Multiplexed gradient descent: Fast online training of modern datasets on hardware neural networks without backpropagation

no code implementations • 5 Mar 2023 • Adam N. McCaughan, Bakhrom G. Oripov, Natesh Ganesh, Sae Woo Nam, Andrew Dienstfrey, Sonia M. Buckley

We present multiplexed gradient descent (MGD), a gradient descent framework designed to easily train analog or digital neural networks in hardware.