1 citations · 2 across the 2 of their papers we have counts for
6 papers
Spikemax: Spike-based Loss Methods for Classification
Sumit Bam Shrestha, Longwei Zhu, Pengfei Sun
Spiking Neural Networks~(SNNs) are a promising research paradigm for low power edge-based computing. Recent works in SNN backpropagation has enabled training of SNNs for practical…
Efficient Neuromorphic Signal Processing with Loihi 2
Garrick Orchard, E. Paxon Frady, Daniel Ben Dayan Rubin +4
The biologically inspired spiking neurons used in neuromorphic computing are nonlinear filters with dynamic state variables -- very different from the stateless neuron models used…
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…
Event-Based Angular Velocity Regression with Spiking Networks
Mathias Gehrig, Sumit Bam Shrestha, Daniel Mouritzen +1
Spiking Neural Networks (SNNs) are bio-inspired networks that process information conveyed as temporal spikes rather than numeric values. A spiking neuron of an SNN only produces a…
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…