Name
Abstract | ||
|---|---|---|
In convolutional neural networks (CNNs), data movement inside convolution layers between memory and PEs is most energy dominant. This paper proposes a convolution processing dataflow that reduces both the number of memory accesses and the on-chip buffer capacity for convolution operations. Based on the dataflow, we design an on-chip buffer-minimized CNN accelerator. Compared with the state-of-the-art CNN accelerator, the proposed CNN accelerator utilizes 2.30 times less on-chip buffer and 2.18 times energy efficiency to achieve the same data throughput under Alexnet. The proposed architecture is able to achieve higher data throughput with the almost constant on-chip buffer capacity.
|
Year | DOI | Venue |
|---|---|---|
2020 | 10.1145/3386263.3406925 | GLSVLSI '20: Great Lakes Symposium on VLSI 2020
Virtual Event
China
September, 2020 |
DocType | ISBN | Citations |
Conference | 978-1-4503-7944-1 | 0 |
PageRank | References | Authors |
0.34 | 0 | 3 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Hongjie Xu | 1 | 0 | 1.69 |
| Shiomi, J. | 2 | 9 | 5.37 |
| Hidetoshi Onodera | 3 | 455 | 105.29 |