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Creators/Authors contains: "Yang, Cheng-Yun"

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  1. ; ; ; ; ; ; ; ; ; ; et al (, 2024 IEEE/ACM International Symposium on Low Power Electronics and Design (ISLPED))
    Accepted Manuscript Full Text Available
  2. ; ; ; ; ; ; ; ; ; (, Asia and South Pacific Design Automation Conference (ASP-DAC))
    Computer vision often uses highly accurate Convolutional Neural Networks (CNNs), but these deep learning models are associated with ever-increasing energy and computation requirements. Producing more energy-efficient CNNs often requires model training which can be cost-prohibitive. We propose a novel, automated method to make a pretrained CNN more energyefficient without re-training. Given a pretrained CNN, we insert a threshold layer that filters activations from the preceding layers to identify regions of the image that are irrelevant, i.e. can be ignored by the following layers while maintaining accuracy. Our modified focused convolution operation saves inference latency (by up to 25%) and energy costs (by up to 22%) on various popular pretrained CNNs, with little to no loss in accuracy 
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    Full Text Available