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cs.LG2025★ 2 cited
Scaling of hardware-compatible perturbative training algorithms
Bakhrom G. Oripov, Andrew Dienstfrey, Adam N. McCaughan +1
In this work, we explore the capabilities of multiplexed gradient descent (MGD), a scalable and efficient perturbative zeroth-order training method for estimating the gradient of a…
cs.LG2023
Machine Learning-powered Compact Modeling of Stochastic Electronic Devices using Mixture Density Networks
Jack Hutchins, Shamiul Alam, Dana S. Rampini +3
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…