Corpus ID: 210064442

Non-Structured DNN Weight Pruning -- Is It Beneficial in Any Platform?.

@article{Ma2019NonStructuredDW,
  title={Non-Structured DNN Weight Pruning -- Is It Beneficial in Any Platform?.},
  author={Xiaolong Ma and Sheng-Fuu Lin and Shaokai Ye and Zhezhi He and Linfeng Zhang and Geng Yuan and Sia huat Tan and Zhengang Li and Deliang Fan and Xuehai Qian and Xue Lin and Kaisheng Ma and Yanzhi Wang},
  journal={arXiv: Learning},
  year={2019}
}
  • Xiaolong Ma, Sheng-Fuu Lin, +10 authors Yanzhi Wang
  • Published 2019
  • Mathematics, Computer Science
  • arXiv: Learning
  • Large deep neural network (DNN) models pose the key challenge to energy efficiency due to the significantly higher energy consumption of off-chip DRAM accesses than arithmetic or SRAM operations. It motivates the intensive research on model compression with two main approaches. Weight pruning leverages the redundancy in the number of weights and can be performed in a non-structured, which has higher flexibility and pruning rate but incurs index accesses due to irregular weights, or structured… CONTINUE READING

    References

    Publications referenced by this paper.
    SHOWING 1-10 OF 123 REFERENCES

    Scalpel: Customizing DNN pruning to the underlying hardware parallelism

    VIEW 2 EXCERPTS

    EIE: Efficient Inference Engine on Compressed Deep Neural Network

    VIEW 1 EXCERPT

    CirCNN: Accelerating and Compressing Deep Neural Networks Using Block-Circulant Weight Matrices

    • Caiwen Ding, Siyu Liao, +13 authors B. Yuan
    • Computer Science
    • 2017 50th Annual IEEE/ACM International Symposium on Microarchitecture (MICRO)
    • 2017
    VIEW 1 EXCERPT

    Bit Fusion: Bit-Level Dynamically Composable Architecture for Accelerating Deep Neural Network

    RANA: Towards Efficient Neural Acceleration with Refresh-Optimized Embedded DRAM