Abstract
Convolutional Neural Network is a promising technology in machine learning. Due to its vast computing and data requirements, it needs to be run with a specific accelerator to achieve reasonable energy efficiency. Improving the performance of accelerators has become the research hotspot. A mixed-precision structure can be used to improve hardware utilization, thus reducing area and power. However, the mixed-precision flow control is so complicated that it costs too much hardware resources. In this paper, a CNN accelerator with embedded RISC-V controllers is introduced to achieve flexible control at a very low cost. The ASIC synthesized results show that the proposed design area with two embedded cores is 5% less than the basic design.
| Original language | English |
|---|---|
| Title of host publication | China Semiconductor Technology International Conference 2021, CSTIC 2021 |
| Editors | Cor Claeys, Steve X. Liang, Qinghuang Lin, Ru Huang, Hanming Wu, Peilin Song, Linyong Pang, Ying Zhang, Beichao Zhang, Xinping Xinping Qu, Cheng Zhuo, Hsiang-Lan Lung |
| Publisher | Institute of Electrical and Electronics Engineers Inc. |
| ISBN (Electronic) | 9781665449458 |
| DOIs | |
| State | Published - 14 Mar 2021 |
| Externally published | Yes |
| Event | 2021 China Semiconductor Technology International Conference, CSTIC 2021 - Shanghai, China Duration: 14 Mar 2021 → 15 Mar 2021 |
Publication series
| Name | China Semiconductor Technology International Conference 2021, CSTIC 2021 |
|---|
Conference
| Conference | 2021 China Semiconductor Technology International Conference, CSTIC 2021 |
|---|---|
| Country/Territory | China |
| City | Shanghai |
| Period | 14/03/21 → 15/03/21 |
UN SDGs
This output contributes to the following UN Sustainable Development Goals (SDGs)
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SDG 7 Affordable and Clean Energy
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