467 citations · 474 across the 12 of their papers we have counts for
15 papers
A Compute and Communication Runtime Model for Loihi 2
Jonathan Timcheck, Alessandro Pierro, Sumit Bam Shrestha
Neuromorphic computers hold the potential to vastly improve the speed and efficiency of a wide range of computational kernels with their asynchronous, compute-memory co-located, sp…
Autonomous Reinforcement Learning Robot Control with Intel's Loihi 2 Neuromorphic Hardware
Kenneth Stewart, Roxana Leontie, Samantha Chapin +3
We present an end-to-end pipeline for deploying reinforcement learning (RL) trained Artificial Neural Networks (ANNs) on neuromorphic hardware by converting them into spiking Sigma…
Neuromorphic Principles for Efficient Large Language Models on Intel Loihi 2
Steven Abreu, Sumit Bam Shrestha, Rui-Jie Zhu +1
Large language models (LLMs) deliver impressive performance but require large amounts of energy. In this work, we present a MatMul-free LLM architecture adapted for Intel's neuromo…
Region Masking to Accelerate Video Processing on Neuromorphic Hardware
Sreetama Sarkar, Sumit Bam Shrestha, Yue Che +3
The rapidly growing demand for on-chip edge intelligence on resource-constrained devices has motivated approaches to reduce energy and latency of deep learning models. Spiking neur…
Accelerating Linear Recurrent Neural Networks for the Edge with Unstructured Sparsity
Alessandro Pierro, Steven Abreu, Jonathan Timcheck +3
Linear recurrent neural networks enable powerful long-range sequence modeling with constant memory usage and time-per-token during inference. These architectures hold promise for s…
A Diagonal Structured State Space Model on Loihi 2 for Efficient Streaming Sequence Processing
Svea Marie Meyer, Philipp Weidel, Philipp Plank +4
Deep State-Space Models (SSM) demonstrate state-of-the art performance on long-range sequence modeling tasks. While the recurrent structure of SSMs can be efficiently implemented a…
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…
Efficient Video and Audio processing with Loihi 2
Sumit Bam Shrestha, Jonathan Timcheck, Paxon Frady +2
Loihi 2 is an asynchronous, brain-inspired research processor that generalizes several fundamental elements of neuromorphic architecture, such as stateful neuron models communicati…
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…
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…