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Lowering Dynamic Power of a Stream-based CNN Hardware Accelerator

Citation Author(s):
Duvindu Piyasena, Rukshan Wickramasinghe, Debdeep Paul, Siew-Kei Lam, Meiqing Wu
Submitted by:
Duvindu Piyasena
Last updated:
26 September 2019 - 8:30pm
Document Type:
Poster
Document Year:
2019
Event:
Paper Code:
6112101

Abstract 

Abstract: 

Custom hardware accelerators of Convolutional Neural Networks (CNN) provide a promising solution to meet real-time constraints for a wide range of applications on low-cost embedded devices. In this work, we aim to lower the dynamic power of a stream-based CNN hardware accelerator by reducing the computational redundancies in the CNN layers. In particular, we investigate the redundancies due to the downsampling effect of max pooling layers which are prevalent in state-of-the-art CNNs, and propose an approximation method to reduce the overall computations. The experimental results show that the proposed method leads to lower dynamic power without sacrificing accuracy.

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