High Accuracy and Low Latency Mixed Precision Neural Network Acceleration for TinyML Applications on Resource-Constrained FPGAs
Author:
Affiliation:
1. Nanyang Technological University,School of Electrical and Electronic Engineering,Singapore
2. Institue of Microelectronics, Agency for Science, Technology and Research (A*STAR),Singapore
Publisher
IEEE
Link
http://xplorestaging.ieee.org/ielx8/10557746/10557828/10558440.pdf?arnumber=10558440
Reference21 articles.
1. Mixed-Precision Neural Networks: A Survey;Rakka,2022
2. FILM-QNN: Efficient FPGA Acceleration of Deep Neural Networks with Intra-Layer, Mixed-Precision Quantization
3. Differentiable Dynamic Quantization with Mixed Precision and Adaptive Resolution;Zhang
4. Quantization and Training of Neural Networks for Efficient Integer-Arithmetic-Only Inference
5. Design and implementation of an efficient CNN accelerator for low-cost FPGAs
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