Redundancy-Reduced MobileNet Acceleration on Reconfigurable Logic for ImageNet Classification
| Autor: | Peter Y. K. Cheung, Julian Faraone, David B. Thomas, Yiren Zhao, Philip H. W. Leong, Junyi Liu, Jiang Su |
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| Rok vydání: | 2018 |
| Předmět: |
010302 applied physics
Contextual image classification Computational complexity theory business.industry Computer science Inference Pattern recognition 02 engineering and technology 01 natural sciences Convolutional neural network 020202 computer hardware & architecture 0103 physical sciences 0202 electrical engineering electronic engineering information engineering Systems design Computer vision algorithms Artificial intelligence Latency (engineering) business Field-programmable gate array |
| Zdroj: | Applied Reconfigurable Computing. Architectures, Tools, and Applications ISBN: 9783319788890 ARC |
| DOI: | 10.1007/978-3-319-78890-6_2 |
| Popis: | 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\(\times \) less parameters, 3.2\(\times \) 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. |
| Databáze: | OpenAIRE |
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