Spiking neural networks (SNNs) represent the third generation of neural networks and are expected to enable new classes of machine learning applications.
This work proposes PEASE, a Programmable Event-driven processor Architecture for SNN Evaluation, a method to map any given SNN to PEASE such that the workload ...
In neuromorphic systems, an efficient way of applying the leakage is by removing a leak value after each neuron update, called leak-upon-load [96] .
Spiking neural networks (SNNs) are composed of neurons that communicate with each other by means of binary spikes. While. PEASE can support SNNs of any topology ...
We present a novel digital architecture for implementing RSNNs with perfect software-to-hardware fidelity. Our results demonstrate accurate inference and low ...
Mar 1, 2024 · We introduce two novel event-driven learning methods: the spike-timing-dependent event-driven (STD-ED) and membrane-potential-dependent event-driven (MPD-ED) ...
We propose a new digital architecture compatible with Recurrent Spiking Neural Networks (RSNNs) trained using the PyTorch framework and Back-Propagation- ...
A Programmable Event-driven Architecture for Evaluating Spiking Neural Networks ... Approximate computing for spiking neural networks. S. Sen, S ...
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In this study, we assess three distinct algorithms proposed for adding a synchronization capability to asynchronous event-driven compute systems.
Nov 26, 2024 · This paper presents a systematic review of datasets, architectures, learning methods, implementation techniques, and evaluation methodologies ...