9 citations · 11 across the 3 of their papers we have counts for
5 papers
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…
Hessian Aware Quantization of Spiking Neural Networks
Hin Wai Lui, Emre Neftci
To achieve the low latency, high throughput, and energy efficiency benefits of Spiking Neural Networks (SNNs), reducing the memory and compute requirements when running on a neurom…
Encoding Event-Based Data With a Hybrid SNN Guided Variational Auto-encoder in Neuromorphic Hardware
Kenneth Stewart, Andreea Danielescu, Timothy Shea +1
Neuromorphic hardware equipped with learning capabilities can adapt to new, real-time data. While models of Spiking Neural Networks (SNNs) can now be trained using gradient descent…
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…
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).…