Parallelism in Deep Learning Accelerators
Publication
, Conference
Song, L; Chen, F; Chen, Y; Li, HH
Published in: Proceedings of the Asia and South Pacific Design Automation Conference, ASP-DAC
January 1, 2020
Deep learning is the core of artificial intelligence and it achieves state-of-the-art in a wide range of applications. The intensity of computation and data in deep learning processing poses significant challenges to the conventional computing platforms. Thus, specialized accelerator architectures are proposed for the acceleration of deep learning. In this paper, we classify the design space of current deep learning accelerators into three levels, (1) processing engine, (2) memory and (3) accelerator, and present a constructive view from a perspective of parallelism in the three levels.
Duke Scholars
Published In
Proceedings of the Asia and South Pacific Design Automation Conference, ASP-DAC
DOI
Publication Date
January 1, 2020
Volume
2020-January
Start / End Page
645 / 650
Citation
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Chicago
ICMJE
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Song, L., Chen, F., Chen, Y., & Li, H. H. (2020). Parallelism in Deep Learning Accelerators. In Proceedings of the Asia and South Pacific Design Automation Conference, ASP-DAC (Vol. 2020-January, pp. 645–650). https://doi.org/10.1109/ASP-DAC47756.2020.9045206
Song, L., F. Chen, Y. Chen, and H. H. Li. “Parallelism in Deep Learning Accelerators.” In Proceedings of the Asia and South Pacific Design Automation Conference, ASP-DAC, 2020-January:645–50, 2020. https://doi.org/10.1109/ASP-DAC47756.2020.9045206.
Song L, Chen F, Chen Y, Li HH. Parallelism in Deep Learning Accelerators. In: Proceedings of the Asia and South Pacific Design Automation Conference, ASP-DAC. 2020. p. 645–50.
Song, L., et al. “Parallelism in Deep Learning Accelerators.” Proceedings of the Asia and South Pacific Design Automation Conference, ASP-DAC, vol. 2020-January, 2020, pp. 645–50. Scopus, doi:10.1109/ASP-DAC47756.2020.9045206.
Song L, Chen F, Chen Y, Li HH. Parallelism in Deep Learning Accelerators. Proceedings of the Asia and South Pacific Design Automation Conference, ASP-DAC. 2020. p. 645–650.
Published In
Proceedings of the Asia and South Pacific Design Automation Conference, ASP-DAC
DOI
Publication Date
January 1, 2020
Volume
2020-January
Start / End Page
645 / 650