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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...
| Autores principales: | , , , , |
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| Formato: | Online Artículo Texto |
| Lenguaje: | English |
| Publicado: |
Frontiers Media S.A.
2020
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| Materias: | |
| Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7689062/ https://www.ncbi.nlm.nih.gov/pubmed/33281549 http://dx.doi.org/10.3389/fnins.2020.598876 |