Search Results for author:
Found 15 papers, 1 papers with code
Speed-based Filtration and DBSCAN of Event-based Camera Data with Neuromorphic Computing
Spiking neural networks are powerful computational elements that pair well with event-based cameras (EBCs).
Multi-level, Forming Free, Bulk Switching Trilayer RRAM for Neuromorphic Computing at the Edge
To address all these challenges, we developed a forming-free and bulk switching RRAM technology based on a trilayer metal-oxide stack.
Spike-based Neuromorphic Computing for Next-Generation Computer Vision
Neuromorphic Computing promises orders of magnitude improvement in energy efficiency compared to traditional von Neumann computing paradigm.
Functional Specification of the RAVENS Neuroprocessor
RAVENS is a neuroprocessor that has been developed by the TENNLab research group at the University of Tennessee.
On-Sensor Data Filtering using Neuromorphic Computing for High Energy Physics Experiments
We present our insights on the various system design choices - from data encoding to optimal hyperparameters of the training algorithm - for an accurate and compact SNN optimized for hardware deployment.
Disclosure of a Neuromorphic Starter Kit
This paper presents a Neuromorphic Starter Kit, which has been designed to help a variety of research groups perform research, exploration and real-world demonstrations of brain-based, neuromorphic processors and hardware environments.
The Case for RISP: A Reduced Instruction Spiking Processor
In this paper, we introduce RISP, a reduced instruction spiking processor.
An Oracle and Observations for the OpenAI Gym / ALE Freeway Environment
The OpenAI Gym project contains hundreds of control problems whose goal is to provide a testbed for reinforcement learning algorithms.
Hyperparameter Optimization in Binary Communication Networks for Neuromorphic Deployment
In this work, we introduce a Bayesian approach for optimizing the hyperparameters of an algorithm for training binary communication networks that can be deployed to neuromorphic hardware.
Multi-Objective Optimization for Size and Resilience of Spiking Neural Networks
In this work, we study Spiking Neural Networks in two neuromorphic architecture implementations with the goal of decreasing their size, while at the same time increasing their resiliency to hardware faults.
Exascale Deep Learning to Accelerate Cancer Research
Using MENNDL--an HPC-enabled software stack for neural architecture search--we generate a neural network with comparable accuracy to state-of-the-art networks on a cancer pathology dataset that is also $16\times$ faster at inference.
Stochasticity and Robustness in Spiking Neural Networks
We then train spiking networks which utilize IF neurons with and without noise and leakage, and experimentally confirm that the noisy networks are more robust.
Spike-based primitives for graph algorithms
In this paper we consider graph algorithms and graphical analysis as a new application for neuromorphic computing platforms.
Deep Learning for Vertex Reconstruction of Neutrino-Nucleus Interaction Events with Combined Energy and Time Data
We present a deep learning approach for vertex reconstruction of neutrino-nucleus interaction events, a problem in the domain of high energy physics.
A Survey of Neuromorphic Computing and Neural Networks in Hardware
Neuromorphic computing has come to refer to a variety of brain-inspired computers, devices, and models that contrast the pervasive von Neumann computer architecture.
