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- W3104393472 abstract "Large-scale deep neural networks (DNNs) are both compute and memory intensive. As the size of DNNs continues to grow, it is critical to improve the energy efficiency and performance while maintaining accuracy. For DNNs, the model size is an important factor affecting performance, scalability and energy efficiency. Weight pruning achieves good compression ratios but suffers from three drawbacks: 1) the irregular network structure after pruning, which affects performance and throughput; 2) the increased training complexity; and 3) the lack of rigirous guarantee of compression ratio and inference accuracy." @default.
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- W3104393472 date "2017-10-14" @default.
- W3104393472 modified "2023-10-18" @default.
- W3104393472 title "C <scp>ir</scp> CNN" @default.
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