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Memory-Efficient Deep Learning on a SpiNNaker 2 Prototype
The memory requirement of deep learning algorithms is considered incompatible with the memory restriction of energy-efficient hardware. A low memory footprint can be achieved by pruning obsolete connections or reducing the precision of connection strengths after the network has been trained. Yet, th...
| Autores principales: | , , , , , , , , , , |
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| Formato: | Online Artículo Texto |
| Lenguaje: | English |
| Publicado: |
Frontiers Media S.A.
2018
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| Materias: | |
| Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6250847/ https://www.ncbi.nlm.nih.gov/pubmed/30505263 http://dx.doi.org/10.3389/fnins.2018.00840 |