Exploring Optimized Spiking Neural Network Architectures for Classification Tasks on Embedded Platfo...
Exploring Optimized Spiking Neural Network Architectures for Classification Tasks on Embedded Platforms
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Publisher
Switzerland: MDPI AG
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English
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Switzerland: MDPI AG
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In recent times, the usage of modern neuromorphic hardware for brain-inspired SNNs has grown exponentially. In the context of sparse input data, they are undertaking low power consumption for event-based neuromorphic hardware, specifically in the deeper layers. However, using deep ANNs for training spiking models is still considered as a tedious ta...
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Exploring Optimized Spiking Neural Network Architectures for Classification Tasks on Embedded Platforms
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TN_cdi_doaj_primary_oai_doaj_org_article_c8855a6ea8e2499cbbce305fb6e43fe5
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https://devfeature-collection.sl.nsw.gov.au/record/TN_cdi_doaj_primary_oai_doaj_org_article_c8855a6ea8e2499cbbce305fb6e43fe5
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ISSN
1424-8220
E-ISSN
1424-8220
DOI
10.3390/s21093240
