Name
Title | ||
|---|---|---|
LSFQ: A Low Precision Full Integer Quantization for High-Performance FPGA-Based CNN Acceleration |
Abstract | ||
|---|---|---|
Neural network quantization has become an important research area. Deep networks run with low precision operations at inference time offer power and space advantages over high precision alternatives, and can maintain high accuracy. However, few quantization can demonstrate this advantage on hardware platform, because the design of quantization algorithm lacks the consideration of actual hardware i... |
Year | DOI | Venue |
|---|---|---|
2021 | 10.1109/COOLCHIPS52128.2021.9410327 | 2021 IEEE Symposium in Low-Power and High-Speed Chips (COOL CHIPS) |
Keywords | DocType | ISSN |
Low Precision Quantization,convolutional neural network (CNN) accelerator,field-programmable gate array (FPGA) | Conference | 2473-4683 |
ISBN | Citations | PageRank |
978-1-6654-1503-3 | 0 | 0.34 |
References | Authors | |
0 | 4 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Zhenshan Bao | 1 | 0 | 2.03 |
| Kang Zhan | 2 | 0 | 0.34 |
| Wenbo Zhang | 3 | 0 | 2.03 |
| Junnan Guo | 4 | 0 | 0.68 |