2.3k citations · 3.2k across the 16 of their papers we have counts for
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Emulating Brain-like Rapid Learning in Neuromorphic Edge Computing
Kenneth Stewart, Michael Neumeier, Sumit Bam Shrestha +2
Achieving personalized intelligence at the edge with real-time learning capabilities holds enormous promise in enhancing our daily experiences and helping decision making, planning…
The Intel Neuromorphic DNS Challenge
Jonathan Timcheck, Sumit Bam Shrestha, Daniel Ben Dayan Rubin +5
A critical enabler for progress in neuromorphic computing research is the ability to transparently evaluate different neuromorphic solutions on important tasks and to compare them…
Online Few-shot Gesture Learning on a Neuromorphic Processor
Kenneth Stewart, Garrick Orchard, Sumit Bam Shrestha +1
We present the Surrogate-gradient Online Error-triggered Learning (SOEL) system for online few-shot learning on neuromorphic processors. The SOEL learning system uses a combination…
Neuromorphic Nearest-Neighbor Search Using Intel's Pohoiki Springs
E. Paxon Frady, Garrick Orchard, David Florey +7
Neuromorphic computing applies insights from neuroscience to uncover innovations in computing technology. In the brain, billions of interconnected neurons perform rapid computation…
On-chip Few-shot Learning with Surrogate Gradient Descent on a Neuromorphic Processor
Kenneth Stewart, Garrick Orchard, Sumit Bam Shrestha +1
Recent work suggests that synaptic plasticity dynamics in biological models of neurons and neuromorphic hardware are compatible with gradient-based learning (Neftci et al., 2019).…
SLAYER: Spike Layer Error Reassignment in Time
Sumit Bam Shrestha, Garrick Orchard
Configuring deep Spiking Neural Networks (SNNs) is an exciting research avenue for low power spike event based computation. However, the spike generation function is non-differenti…