Towards Convolutional Neural Network Acceleration and Compression Based on Simon k-Means

Convolutional Neural Networks (CNNs) are popular models that are widely used in image classification, target recognition, and other fields. Model compression is a common step in transplanting neural networks into embedded devices, and it is often used in the retraining stage. However, it requires a high expenditure of time by retraining weight data to atone for the loss of precision. Unlike in prior designs, we propose a novel model compression approach based on Simon k-means, which is specifically designed to support a hardware acceleration scheme. First, we propose an extension algorithm named Simon k-means based on simple k-means. We use Simon k-means to cluster trained weights in convolutional layers and fully connected layers. Second, we reduce the consumption of hardware resources in data movement and storage by using a data storage and index approach. Finally, we provide the hardware implementation of the compressed CNN accelerator. Our evaluations on several classifications show that our design can achieve 5.27x compression and reduce 74.3% of the multiply-accumulate (MAC) operations in AlexNet on the FASHION-MNIST dataset.