Abstract
This work presents a dynamic energy reduction approach for hardware accelerators for convolutional neural networks (CNN). Two methods are used: (1) an adaptive data-dependent scheme to selectively activate a subset of all neurons, by narrowing down the possible activated classes (2) static bitwidth reduction. The former is applied in late layers of the CNN, while the latter is more effective in early layers. Even accounting for the implementation overheads, the results show 20%-25% energy savings with 5-10% accuracy loss.
Original language | English (US) |
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Title of host publication | ASP-DAC 2019 - 24th Asia and South Pacific Design Automation Conference |
Publisher | Institute of Electrical and Electronics Engineers Inc. |
Pages | 526-531 |
Number of pages | 6 |
ISBN (Electronic) | 9781450360074 |
DOIs | |
State | Published - Jan 21 2019 |
Event | 24th Asia and South Pacific Design Automation Conference, ASPDAC 2019 - Tokyo, Japan Duration: Jan 21 2019 → Jan 24 2019 |
Publication series
Name | Proceedings of the Asia and South Pacific Design Automation Conference, ASP-DAC |
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Other
Other | 24th Asia and South Pacific Design Automation Conference, ASPDAC 2019 |
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Country/Territory | Japan |
City | Tokyo |
Period | 1/21/19 → 1/24/19 |
Bibliographical note
Publisher Copyright:© 2019 Copyright is held by the owner/author(s). Publication rights licensed to ACM.