Web: http://arxiv.org/abs/2206.00843

June 20, 2022, 1:13 a.m. | Yonggan Fu, Haichuan Yang, Jiayi Yuan, Meng Li, Cheng Wan, Raghuraman Krishnamoorthi, Vikas Chandra, Yingyan Lin

cs.CV updates on arXiv.org arxiv.org

Efficient deep neural network (DNN) models equipped with compact operators
(e.g., depthwise convolutions) have shown great potential in reducing DNNs'
theoretical complexity (e.g., the total number of weights/operations) while
maintaining a decent model accuracy. However, existing efficient DNNs are still
limited in fulfilling their promise in boosting real-hardware efficiency, due
to their commonly adopted compact operators' low hardware utilization. In this
work, we open up a new compression paradigm for developing real-hardware
efficient DNNs, leading to boosted hardware efficiency while …

arxiv boosting compression hardware lg networks neural neural networks

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