Redundancy-reduced MobileNet acceleration on reconfigurable logic for ImageNet classification

Su, Jiang, Faraone, Julian, Liu, Junyi, Zhao, Yiren, Thomas, David B., Leong, Philip H.W. and Cheung, Peter Y.K. (2018) Redundancy-reduced MobileNet acceleration on reconfigurable logic for ImageNet classification. Voros, Nikolaos, Keramidas, Georgios, Antonopoulos, Christos, Huebner, Michael, Diniz, Pedro C. and Goehringer, Diana (eds.) In Applied Reconfigurable Computing: Architectures, Tools, and Applications - 14th International Symposium, ARC 2018, Proceedings. vol. 10824 LNCS, Springer. pp. 16-28 . (doi:10.1007/978-3-319-78890-6_2).

Abstract

Modern Convolutional Neural Networks (CNNs) excel in image classification and recognition applications on large-scale datasets such as ImageNet, compared to many conventional feature-based computer vision algorithms. However, the high computational complexity of CNN models can lead to low system performance in power-efficient applications. In this work, we firstly highlight two levels of model redundancy which widely exist in modern CNNs. Additionally, we use MobileNet as a design example and propose an efficient system design for a Redundancy-Reduced MobileNet (RR-MobileNet) in which off-chip memory traffic is only used for inputs/outputs transfer while parameters and intermediate values are saved in on-chip BRAM blocks. Compared to AlexNet, our RR-mobileNet has 25 × less parameters, 3.2 × less operations per image inference but 9%/5.2% higher Top1/Top5 classification accuracy on ImageNet classification task. The latency of a single image inference is only 7.85 ms.

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Contributors

Author: David B. Thomas

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