A Heuristic Exploration of Retraining-free Weight-Sharing for CNN Compression

Abstract

The computational workload involved in Convolutional Neural Networks (CNNs) is typically out of reach for low-power embedded devices. The scientific literature provides a large number of approximation techniques to address this problem. Among them, the Weight-Sharing (WS) technique gives promising results, but it requires carefully determining the shared values for each layer of a given CNN. As the number of possible solutions grows exponentially with the number of layers, the WS Design Space Exploration (DSE) time can easily explode for state-of-the-art CNNs. In this paper, we propose a new heuristic approach to drastically reduce the exploration time without sacrificing the quality of the output. The results carried out on recent CNNs (GoogleNet [1], ResNet50V2 [2], MobileNetV2 [3], InceptionV3 [4], and EfficientNet [5]), trained with the ImageNet [6] dataset, show over 5× memory compression at an acceptable accuracy loss (complying with the MLPerf [7] quality target) without any retraining step and in less than 10 hours. Our code is publicly available on GitHub [8].