184 citations · 1.1k across the 33 of their papers we have counts for
33 papers
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…
Roadmap on Neuromorphic Photonics
Daniel Brunner, Bhavin J. Shastri, Mohammed A. Al Qadasi +147
This roadmap consolidates recent advances while exploring emerging applications, reflecting the remarkable diversity of hardware platforms, neuromorphic concepts, and implementatio…
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…
A superconducting-nanowire single-photon camera with 400,000 pixels
Bakhrom G. Oripov, Dana S. Rampini, Jason Allmaras +4
For the last 50 years, superconducting detectors have offered exceptional sensitivity and speed for detecting faint electromagnetic signals in a wide range of applications. These d…
Cryogenic Reconfigurable Logic with Superconducting Heater Cryotron: Enhancing Area Efficiency and Enabling Camouflaged Processors
Shamiul Alam, Dana S. Rampini, Bakhrom G. Oripov +2
Superconducting electronics are among the most promising alternatives to conventional CMOS technology thanks to the ultra-fast speed and ultra-high energy efficiency of the superco…
Multiplexed gradient descent: Fast online training of modern datasets on hardware neural networks without backpropagation
Adam N. McCaughan, Bakhrom G. Oripov, Natesh Ganesh +3
We present multiplexed gradient descent (MGD), a gradient descent framework designed to easily train analog or digital neural networks in hardware. MGD utilizes zero-order optimiza…