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Hardware-Efficient On-line Learning through Pipelined Truncated-Error Backpropagation in Binary-State Networks
Artificial neural networks (ANNs) trained using backpropagation are powerful learning architectures that have achieved state-of-the-art performance in various benchmarks. Significant effort has been devoted to developing custom silicon devices to accelerate inference in ANNs. Accelerating the traini...
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| Publicado no: | Front Neurosci |
|---|---|
| Main Authors: | , , , |
| Formato: | Artigo |
| Idioma: | Inglês |
| Publicado em: |
Frontiers Media S.A.
2017
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| Assuntos: | |
| Acesso em linha: | https://ncbi.nlm.nih.gov/pmc/articles/PMC5592276/ https://ncbi.nlm.nih.gov/pubmed/28932180 https://ncbi.nlm.nih.govhttp://dx.doi.org/10.3389/fnins.2017.00496 |
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