Neuromorphic implementation of motion detection using oscillation interference
Sign Up to like & getrecommendations! Published in 2020 at "Neurocomputing"
DOI: 10.1016/j.neucom.2019.09.072
Abstract: Abstract Motion detection is paramount for computational vision processing. This is however a particularly challenging task for a neuromorphic hardware in which algorithms are based on interconnected spiking entities, as the instantaneous visual stimuli reports… read more here.
Keywords: motion; neuromorphic implementation; implementation motion; motion detection ... See more keywords
Efficient Neuromorphic Hardware Through Spiking Temporal Online Local Learning
Sign Up to like & getrecommendations! Published in 2022 at "IEEE Transactions on Very Large Scale Integration (VLSI) Systems"
DOI: 10.1109/tvlsi.2022.3208191
Abstract: Local learning schemes have shown promising performance in spiking neural networks (SNNs) training and are considered a step toward more biologically plausible learning. Despite many efforts to design high-performance neuromorphic systems, a fast and efficient… read more here.
Keywords: neuromorphic hardware; hardware; training; local learning ... See more keywords
Mapping and Validating a Point Neuron Model on Intel's Neuromorphic Hardware Loihi
Sign Up to like & getrecommendations! Published in 2022 at "Frontiers in Neuroinformatics"
DOI: 10.3389/fninf.2022.883360
Abstract: Neuromorphic hardware is based on emulating the natural biological structure of the brain. Since its computational model is similar to standard neural models, it could serve as a computational accelerator for research projects in the… read more here.
Keywords: mapping validating; neuromorphic hardware; hardware; intel ... See more keywords
Beyond LIF Neurons on Neuromorphic Hardware
Sign Up to like & getrecommendations! Published in 2022 at "Frontiers in Neuroscience"
DOI: 10.3389/fnins.2022.881598
Abstract: Neuromorphic systems aim to provide accelerated low-power simulation of Spiking Neural Networks (SNNs), typically featuring simple and efficient neuron models such as the Leaky Integrate-and-Fire (LIF) model. Biologically plausible neuron models developed by neuroscientists are… read more here.
Keywords: neuron; hardware; neuromorphic hardware; neuron models ... See more keywords
A Scalable Approach to Modeling on Accelerated Neuromorphic Hardware
Sign Up to like & getrecommendations! Published in 2022 at "Frontiers in Neuroscience"
DOI: 10.3389/fnins.2022.884128
Abstract: Neuromorphic systems open up opportunities to enlarge the explorative space for computational research. However, it is often challenging to unite efficiency and usability. This work presents the software aspects of this endeavor for the BrainScaleS-2… read more here.
Keywords: software; system; accelerated neuromorphic; neuromorphic hardware ... See more keywords
Approaching the mapping limit with closed-loop mapping strategy for deploying neural networks on neuromorphic hardware
Sign Up to like & getrecommendations! Published in 2023 at "Frontiers in Neuroscience"
DOI: 10.3389/fnins.2023.1168864
Abstract: The decentralized manycore architecture is broadly adopted by neuromorphic chips for its high computing parallelism and memory locality. However, the fragmented memories and decentralized execution make it hard to deploy neural network models onto neuromorphic… read more here.
Keywords: neuromorphic hardware; limit; mapping limit; closed loop ... See more keywords