2 citations · 2 across the 4 of their papers we have counts for
3 papers · 1 filter
Hardware-aware Pruning of DNNs using LFSR-Generated Pseudo-Random Indices
Foroozan Karimzadeh, Ningyuan Cao, Brian Crafton +2
Deep neural networks (DNNs) have been emerged as the state-of-the-art algorithms in broad range of applications. To reduce the memory foot-print of DNNs, in particular for embedded…
Quantum-statistical transport phenomena in memristive computing architectures
Christopher N. Singh, Brian A. Crafton, Mathew P. West +8
The advent of reliable, nanoscale memristive components is promising for next generation compute-in-memory paradigms, however, the intrinsic variability in these devices has preven…
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…