Design Considerations for Efficient Deep Neural Networks on Processing-in-Memory Accelerators
© 2019 IEEE. This paper describes various design considerations for deep neural networks that enable them to operate efficiently and accurately on processing-in-memory accelerators. We highlight important properties of these accelerators and the resulting design considerations using experiments cond...
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| Format: | Article |
| Language: | English |
| Published: |
Institute of Electrical and Electronics Engineers (IEEE),
2021-11-15T20:37:23Z.
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| Subjects: | |
| Online Access: | Get fulltext |
| Summary: | © 2019 IEEE. This paper describes various design considerations for deep neural networks that enable them to operate efficiently and accurately on processing-in-memory accelerators. We highlight important properties of these accelerators and the resulting design considerations using experiments conducted on various state-of-the- art deep neural networks with the large-scale ImageNet dataset. NSF (Grant E2CDA 1639921) |
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