114 citations · 227 across the 4 of their papers we have counts for
5 papers
Direct Feedback Alignment with Sparse Connections for Local Learning
Brian Crafton, Abhinav Parihar, Evan Gebhardt +1
Recent advances in deep neural networks (DNNs) owe their success to training algorithms that use backpropagation and gradient-descent. Backpropagation, while highly effective on vo…
Stochastic IMT (insulator-metal-transition) neurons: An interplay of thermal and threshold noise at bifurcation
Abhinav Parihar, Matthew Jerry, Suman Datta +1
Artificial neural networks can harness stochasticity in multiple ways to enable a vast class of computationally powerful models. Electronic implementation of such stochastic networ…
Vertex coloring of graphs via phase dynamics of coupled oscillatory networks
Abhinav Parihar, Nikhil Shukla, Matthew Jerry +2
While Boolean logic has been the backbone of digital information processing, there are classes of computationally hard problems wherein this conventional paradigm is fundamentally…
Computing with Dynamical Systems Based on Insulator-Metal-Transition Oscillators
Abhinav Parihar, Nikhil Shukla, Matthew Jerry +2
In this paper we review recent work on novel computing paradigms using coupled oscillatory dynamical systems. We explore systems of relaxation oscillators based on linear state tra…
Synchronization of pairwise-coupled, identical, relaxation oscillators based on metal-insulator phase transition devices: A Model Study
Abhinav Parihar, Nikhil Shukla, Suman Datta +1
Computing with networks of synchronous oscillators has attracted wide-spread attention as novel materials and device topologies have enabled realization of compact, scalable and lo…