Search Results for author:
Found 9 papers, 4 papers with code
Efficient Video and Audio processing with Loihi 2
Loihi 2 is an asynchronous, brain-inspired research processor that generalizes several fundamental elements of neuromorphic architecture, such as stateful neuron models communicating with event-driven spikes, in order to address limitations of the first generation Loihi.
NeuroBench: A Framework for Benchmarking Neuromorphic Computing Algorithms and Systems
The NeuroBench framework introduces a common set of tools and systematic methodology for inclusive benchmark measurement, delivering an objective reference framework for quantifying neuromorphic approaches in both hardware-independent (algorithm track) and hardware-dependent (system track) settings.
The Intel Neuromorphic DNS Challenge
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 to state-of-the-art conventional solutions.
Spikemax: Spike-based Loss Methods for Classification
In addition, we demonstrate state of the art performances on these datasets, achieving faster inference speed and less energy consumption.
Efficient Neuromorphic Signal Processing with Loihi 2
The biologically inspired spiking neurons used in neuromorphic computing are nonlinear filters with dynamic state variables -- very different from the stateless neuron models used in deep learning.
Online Few-shot Gesture Learning on a Neuromorphic Processor
We present the Surrogate-gradient Online Error-triggered Learning (SOEL) system for online few-shot learning on neuromorphic processors.
Event-Based Angular Velocity Regression with Spiking Networks
Due to their spike-based computational model, SNNs can process output from event-based, asynchronous sensors without any pre-processing at extremely lower power unlike standard artificial neural networks.
On-chip Few-shot Learning with Surrogate Gradient Descent on a Neuromorphic Processor
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
Configuring deep Spiking Neural Networks (SNNs) is an exciting research avenue for low power spike event based computation.
