An Analytical Estimation of Spiking Neural Networks Energy Efficiency

Lemaire, Edgar; Cordone, Loïc; Castagnetti, Andréa; Novac, Pierre-Emmanuel; Courtois, Jonathan; Miramond, Benoît

doi:10.1007/978-3-031-30105-6_48

Cited by 7 publications

(1 citation statement)

References 17 publications

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“…The increasing number of adoptions by the community is symbolic of the success of a framework. Since being open-sourced in December 2019, SpikingJelly has been widely used in many spiking deep learning studies, including adversarial attack (100, 101), ANN2SNN (95,(102)(103)(104)(105)(106), attention mechanisms (107,108), depth estimation from DVS data (69,109), development of innovative materials (110), emotion recognition (111), energy estimation (112), eventbased video reconstruction (113), fault diagnosis (114), hardware design (115)(116)(117), network structure improvements (60,61,(118)(119)(120)(121), spiking neuron improvements (56,(122)(123)(124)(125)(126)(127), training method improvements (128)(129)(130)(131)(132)(133)(134)(135)(136)(137)(138), medical diagnosis (139,140), network pruning …”

Section: Adoptions By the Communitymentioning

confidence: 99%

SpikingJelly: An open-source machine learning infrastructure platform for spike-based intelligence

Fang,

Chen,

Ding

et al. 2023

Sci. Adv.

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Spiking neural networks (SNNs) aim to realize brain-inspired intelligence on neuromorphic chips with high energy efficiency by introducing neural dynamics and spike properties. As the emerging spiking deep learning paradigm attracts increasing interest, traditional programming frameworks cannot meet the demands of the automatic differentiation, parallel computation acceleration, and high integration of processing neuromorphic datasets and deployment. In this work, we present the SpikingJelly framework to address the aforementioned dilemma. We contribute a full-stack toolkit for preprocessing neuromorphic datasets, building deep SNNs, optimizing their parameters, and deploying SNNs on neuromorphic chips. Compared to existing methods, the training of deep SNNs can be accelerated 11×, and the superior extensibility and flexibility of SpikingJelly enable users to accelerate custom models at low costs through multilevel inheritance and semiautomatic code generation. SpikingJelly paves the way for synthesizing truly energy-efficient SNN-based machine intelligence systems, which will enrich the ecology of neuromorphic computing.

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