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Creators/Authors contains: "Li, Sicheng"

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  1. ; ; ; ; ; ; ( , the 23rd International Conference on Medical Image Computing and Computer Assisted Intervention (MICCAI) 2020)
    Accepted Manuscript Full Text Available
  2. ; ; ; ; ; ; ; ( , the 23rd International Conference on Medical Image Computing and Computer Assisted Intervention (MICCAI))
    Accepted Manuscript Full Text Available
  3. ; ; ; ; ; ( , Asia and South Pacific Design Automation Conference (ASP-DAC))
    Deep Neural Networks (DNNs) are pervasively applied in many artificial intelligence (AI) applications. The high performance of DNNs comes at the cost of larger size and higher compute complexity. Recent studies show that DNNs have much redundancy, such as the zero-value parameters and excessive numerical precision. To reduce computing complexity, many redundancy reduction techniques have been proposed, including pruning and data quantization. In this paper, we demonstrate our cooptimization of the DNN algorithm and hardware which exploits the model redundancy to accelerate DNNs. 
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  4. ; ; ; ; ( , the Proceedings of the 14th ACM/IEEE Symposium on Embedded Systems for Real-Time Multimedia (ESTIMedia 16))
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