Redundancy-Reduced MobileNet Acceleration on Reconfigurable Logic for ImageNet Classification

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$\times$ 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.