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Unsupervised Adaptive Weight Pruning for Energy-Efficient Neuromorphic Systems
To tackle real-world challenges, deep and complex neural networks are generally used with a massive number of parameters, which require large memory size, extensive computational operations, and high energy consumption in neuromorphic hardware systems. In this work, we propose an unsupervised online...
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| Publicado no: | Front Neurosci |
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| Main Authors: | , , , , |
| Formato: | Artigo |
| Idioma: | Inglês |
| Publicado em: |
Frontiers Media S.A.
2020
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| Assuntos: | |
| Acesso em linha: | https://ncbi.nlm.nih.gov/pmc/articles/PMC7689062/ https://ncbi.nlm.nih.gov/pubmed/33281549 https://ncbi.nlm.nih.govhttp://dx.doi.org/10.3389/fnins.2020.598876 |
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