Feb 13, 2020 · In this paper, a continuous-valued artificial neural network (ANN) with fully connections is equivalently converted into spiking operations and ...
ABSTRACT. Spiking neural networks (SNNs) are potentially an efficient way to reduce the computation load as well as the power consumption on edge devices ...
In our toolchain, a user will first convert trained artificial neural networks (ANNs) to SNNs [46] , [47], map the models to chips according to their hardware ...
May 27, 2021 · In this paper, we analyze the reasons for the performance loss and propose a novel bistable spiking neural network (BSNN) that addresses the ...
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Jan 12, 2021 · One-sentence Summary: We propose and validate an optimal pipeline that efficiently converts conventional artificial neural networks to spiking ...
In this paper, we present a framework named DNISNM for converting ANN to SNN, with the aim of addressing conversion errors arising from differences in the ...
Mar 29, 2019 · 32 We present a proof of concept demonstration of ANN–SNN conversion by mapping the weights of a pre-trained ANN to neurons that can oscillate ...
Jan 16, 2016 · In this work we present a train-and-constrain methodology that enables the mapping of machine learned (Elman) RNNs on a substrate of spiking neurons.
This toolbox automates the conversion of pre-trained analog to spiking neural networks (ANN to SNN), and provides tools for testing the SNNs in spiking neuron ...
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In this survey, we present the state of the art of hardware implementations of spiking neural networks and the current trends in algorithm elaboration.