Biography

I am pursuing my M.S. degree in College of Control Science and Engineering, Zhejiang University, Hangzhou, China. My major research interest is intelligent quadruped locomotion and reinforcement learning.

Research and Interests

• Intelligent quadruped locomotion
• Reinforcement learning

Publications

• Siqi Li, Jun Chen, Shanqi Liu, Chengrui Zhu, Guanzhong Tian, and Yong Liu. MCMC: Multi-Constrained Model Compression via One-stage Envelope Reinforcement Learning. IEEE Transactions on Neural Networks and Learning Systems, 2024.
  [BibTeX] [Abstract] [DOI]

  Model compression methods are being developed to bridge the gap between the massive scale of neural networks and the limited hardware resources on edge devices. Since most real-world applications deployed on resource-limited hardware platforms typically have multiple hardware constraints simultaneously, most existing model compression approaches that only consider optimizing one single hardware objective are ineffective. In this article, we propose an automated pruning method called multi-constrained model compression (MCMC) that allows for the optimization of multiple hardware targets, such as latency, floating point operations (FLOPs), and memory usage, while minimizing the impact on accuracy. Specifically, we propose an improved multi-objective reinforcement learning (MORL) algorithm, the one-stage envelope deep deterministic policy gradient (DDPG) algorithm, to determine the pruning strategy for neural networks. Our improved one-stage envelope DDPG algorithm reduces exploration time and offers greater flexibility in adjusting target priorities, enhancing its suitability for pruning tasks. For instance, on the visual geometry group (VGG)-16 network, our method achieved an 80% reduction in FLOPs, a 2.31x reduction in memory usage, and a 1.92x acceleration, with an accuracy improvement of 0.09% compared with the baseline. For larger datasets, such as ImageNet, we reduced FLOPs by 50% for MobileNet-V1, resulting in a 4.7x faster speed and 1.48x memory compression, while maintaining the same accuracy. When applied to edge devices, such as JETSON XAVIER NX, our method resulted in a 71% reduction in FLOPs for MobileNet-V1, leading to a 1.63x faster speed, 1.64x memory compression, and an accuracy improvement.

  @article{li2024mcmc,
  title = {MCMC: Multi-Constrained Model Compression via One-stage Envelope Reinforcement Learning},
  author = {Siqi Li and Jun Chen and Shanqi Liu and Chengrui Zhu and Guanzhong Tian and Yong Liu},
  year = 2024,
  journal = {IEEE Transactions on Neural Networks and Learning Systems},
  doi = {10.1109/TNNLS.2024.3353763},
  abstract = {Model compression methods are being developed to bridge the gap between the massive scale of neural networks and the limited hardware resources on edge devices. Since most real-world applications deployed on resource-limited hardware platforms typically have multiple hardware constraints simultaneously, most existing model compression approaches that only consider optimizing one single hardware objective are ineffective. In this article, we propose an automated pruning method called multi-constrained model compression (MCMC) that allows for the optimization of multiple hardware targets, such as latency, floating point operations (FLOPs), and memory usage, while minimizing the impact on accuracy. Specifically, we propose an improved multi-objective reinforcement learning (MORL) algorithm, the one-stage envelope deep deterministic policy gradient (DDPG) algorithm, to determine the pruning strategy for neural networks. Our improved one-stage envelope DDPG algorithm reduces exploration time and offers greater flexibility in adjusting target priorities, enhancing its suitability for pruning tasks. For instance, on the visual geometry group (VGG)-16 network, our method achieved an 80% reduction in FLOPs, a 2.31x reduction in memory usage, and a 1.92x acceleration, with an accuracy improvement of 0.09% compared with the baseline. For larger datasets, such as ImageNet, we reduced FLOPs by 50% for MobileNet-V1, resulting in a 4.7x faster speed and 1.48x memory compression, while maintaining the same accuracy. When applied to edge devices, such as JETSON XAVIER NX, our method resulted in a 71% reduction in FLOPs for MobileNet-V1, leading to a 1.63x faster speed, 1.64x memory compression, and an accuracy improvement.}
  }