Biography

I am pursuing my M.S. degree in College of Control Science and Engineering, Zhejiang University, Hangzhou, China. My major research interest isnetwork quantization and FPGA.

Research and Interests

• Deep Learning
• Network compression
• Network quantization

Publications

• Bofeng Jiang, Jun Chen, and Yong Liu. Single-Shot Pruning and Quantization for Hardware-Friendly Neural Network Acceleration. Engineering Applications of Artificial Intelligence, 126, 2023.
  [BibTeX] [Abstract] [DOI] [PDF]

  Applying CNN on embedded systems is challenging due to model size limitations. Pruning and quantization can help, but are time-consuming to apply separately. Our Single-Shot Pruning and Quantization strategy addresses these issues by quantizing and pruning in a single process. We evaluated our method on CIFAR-10 and CIFAR-100 datasets for image classification. Our model is 69.4% smaller with little accuracy loss, and runs 6-8 times faster on NVIDIA Xavier NX hardware.

  @article{jiang2023ssp,
  title = {Single-Shot Pruning and Quantization for Hardware-Friendly Neural Network Acceleration},
  author = {Bofeng Jiang and Jun Chen and Yong Liu},
  year = 2023,
  journal = {Engineering Applications of Artificial Intelligence},
  volume = 126,
  doi = {10.1016/j.engappai.2023.106816},
  abstract = {Applying CNN on embedded systems is challenging due to model size limitations. Pruning and quantization can help, but are time-consuming to apply separately. Our Single-Shot Pruning and Quantization strategy addresses these issues by quantizing and pruning in a single process. We evaluated our method on CIFAR-10 and CIFAR-100 datasets for image classification. Our model is 69.4% smaller with little accuracy loss, and runs 6-8 times faster on NVIDIA Xavier NX hardware.}
  }