Publications
Detailed Information
DANCE: Differentiable Accelerator/Network Co-Exploration
Cited 16 time in
Web of Science
Cited 20 time in Scopus
- Authors
- Issue Date
- 2021-11
- Publisher
- IEEE
- Citation
- 2021 58TH ACM/IEEE DESIGN AUTOMATION CONFERENCE (DAC), Vol.2021-December, pp.337-342
- Abstract
- This work presents DANCE, a differentiable approach towards the co-exploration of hardware accelerator and network architecture design. At the heart of DANCE is a differentiable evaluator network. By modeling the hardware evaluation software with a neural network, the relation between the accelerator design and the hardware metrics becomes differentiable, allowing the search to be performed with backpropagation. Compared to the naive existing approaches, our method performs co-exploration in a significantly shorter time, while achieving superior accuracy and hardware cost metrics.
- ISSN
- 0738-100X
- Files in This Item:
- There are no files associated with this item.
Related Researcher
- College of Engineering
- Department of Electrical and Computer Engineering
Item View & Download Count
Items in S-Space are protected by copyright, with all rights reserved, unless otherwise indicated.