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Algorithm-hardware co-design of binary neural network for efficient super resolution on FPGA | Synapse
March 3, 2026
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Algorithm-hardware co-design of binary neural network for efficient super resolution on FPGA
YS
Yuanxin Su
Xi’an Jiaotong-Liverpool University
YW
Yihong Wang
Xi’an Jiaotong-Liverpool University
YP
Yushan Pan
Xi’an Jiaotong-Liverpool University
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Key Points
The efficient super resolution achieved on FPGA demonstrates significant improvements in processing speed.
Notably, the algorithm, combined with hardware co-design, allows for enhanced performance in neural network tasks.
Analysis involved a systematic approach using binary neural networks to optimize FPGA resources for better applications.
This co-design approach highlights the potential benefits of merging algorithm development with hardware specifications for performance.
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Su et al. (Tue,) studied this question.
synapsesocial.com/papers/69a75ff4c6e9836116a2c544
https://doi.org/https://doi.org/10.1016/j.vlsi.2026.102674
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