Energy-Efficient Convolutional Neural Networks via Recurrent Data Reuse

Deep learning (DL) algorithms have substantially improved in terms of accuracy and efficiency. Convolutional Neural Networks (CNNs) are now able to outperform traditional algorithms in computer vision tasks such as object classification, detection, recognition, and image segmentation. They represent an attractive solution for many embedded applications which may take advantage from machine-learning at the edge. Needless to say, the key to success lies under the availability of efficient hardware implementations which meet the stringent design constraints. Inspired by the way human brains process information, this paper presents a method that improves the processing efficiency of CNNs leveraging their repetitiveness. More specifically, we introduce (i) a clustering methodology that maximizes weights/activation reuse, and (ii) the design of a heterogeneous processing element which integrates a Floating-Point Unit (FPU) with an associative memory that manages recurrent patterns. The experimental analysis reveals that the proposed method achieves substantial energy savings with low accuracy loss, thus providing a practical design option that might find application in the growing segment of edge-computing.