抄録
The state-of-the-art customized accelerators of convolution neural networks (CNN) have achieved high throughput while the huge amount of data movements still remains as the dominant part of the total energy costs. In this paper, we propose an energy-efficient scheduling approach to find an efficient dataflow that minimizes data movements with limited hardware resource budgets. In detail, two-level nested loop transformations are proposed to separate memory and computing resource constraints. This allows us to fully exploit the potential of available memory resources for reducing off-chip memory traffic. Further, the proposed cross-loop model is capable of figuring out the data locality across nested loops in CNN algorithms. Finally, energy-delay production is employed as the evaluation criteria to balancing energy and throughput performance. The experimental results show our cross-loop model can reduce the off-chip data movements by 11-21% and achieve the theoretical optimum. Therefore, the proposed scheduling method can increase the energy efficiency by at least 8.7 times.
本文言語 | English |
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ホスト出版物のタイトル | IEEE International Symposium on Circuits and Systems |
ホスト出版物のサブタイトル | From Dreams to Innovation, ISCAS 2017 - Conference Proceedings |
出版社 | Institute of Electrical and Electronics Engineers Inc. |
ISBN(電子版) | 9781467368520 |
DOI | |
出版ステータス | Published - 2017 9月 25 |
イベント | 50th IEEE International Symposium on Circuits and Systems, ISCAS 2017 - Baltimore, United States 継続期間: 2017 5月 28 → 2017 5月 31 |
Other
Other | 50th IEEE International Symposium on Circuits and Systems, ISCAS 2017 |
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国/地域 | United States |
City | Baltimore |
Period | 17/5/28 → 17/5/31 |
ASJC Scopus subject areas
- 電子工学および電気工学