TECO: A Unified Feature Map Compression Framework Based on Transform and Entropy

The massive memory accesses of feature maps (FMs) in deep neural network (DNN) processors lead to huge power consumption, which becomes a major energy bottleneck of DNN accelerators. In this article, we propose a unified framework named Transform and Entropy-based COmpression (TECO) scheme to efficiently compress FMs with various attributes in DNN inference. We explore, for the first time, the intrinsic unimodal distribution characteristic that widely exists in the frequency domain of various FMs. In addition, a well-optimized hardware-friendly coding scheme is designed, which fully utilizes this remarkable data distribution characteristic to encode and compress the frequency spectrum of different FMs. Furthermore, the information entropy theory is leveraged to develop a novel loss function for improving the compression ratio and to make a fast comparison among different compressors. Extensive experiments are performed on multiple tasks and demonstrate that the proposed TECO achieves compression ratios of 2.31x in ResNet-50 on image classification, 3.47x in UNet on dark image enhancement, and 3.18x in Yolo-v4 on object detection while keeping the accuracy of these models. Compared with the upper limit of the compression ratio for original FMs, the proposed framework achieves the compression ratio improvement of 21%, 157%, and 152% on the above models.