440 citations · 622 across the 13 of their papers we have counts for
5 papers · 1 filter
Enabling Deep Spiking Neural Networks with Hybrid Conversion and Spike Timing Dependent Backpropagation
Nitin Rathi, Gopalakrishnan Srinivasan, Priyadarshini Panda +1
Spiking Neural Networks (SNNs) operate with asynchronous discrete events (or spikes) which can potentially lead to higher energy-efficiency in neuromorphic hardware implementations…
RMP-SNN: Residual Membrane Potential Neuron for Enabling Deeper High-Accuracy and Low-Latency Spiking Neural Network
Bing Han, Gopalakrishnan Srinivasan, Kaushik Roy
Spiking Neural Networks (SNNs) have recently attracted significant research interest as the third generation of artificial neural networks that can enable low-power event-driven da…
Pruning Filters while Training for Efficiently Optimizing Deep Learning Networks
Sourjya Roy, Priyadarshini Panda, Gopalakrishnan Srinivasan +1
Modern deep networks have millions to billions of parameters, which leads to high memory and energy requirements during training as well as during inference on resource-constrained…
Explicitly Trained Spiking Sparsity in Spiking Neural Networks with Backpropagation
Jason M. Allred, Steven J. Spencer, Gopalakrishnan Srinivasan +1
Spiking Neural Networks (SNNs) are being explored for their potential energy efficiency resulting from sparse, event-driven computations. Many recent works have demonstrated effect…
sBSNN: Stochastic-Bits Enabled Binary Spiking Neural Network with On-Chip Learning for Energy Efficient Neuromorphic Computing at the Edge
Minsuk Koo, Gopalakrishnan Srinivasan, Yong Shim +1
In this work, we propose stochastic Binary Spiking Neural Network (sBSNN) composed of stochastic spiking neurons and binary synapses (stochastic only during training) that computes…