Hardware-Efficient Lightweight Feature Map Compression for Convolutional Neural Networks

Convolutional neural networks (CNNs) are widely used in computer vision and other fields, while their model parameters and intermediate feature map sizes continue to grow significantly. When deployed on resource-constrained embedded platforms, the frequent data movement of feature maps causes substantial energy consumption and memory bandwidth pressure that demands urgent solutions. To address this challenge, this paper proposes a lightweight feature compression scheme that effectively compresses feature maps through dynamic uniform quantization combined with exponential-Golomb entropy coding. The corresponding hardware architecture is introduced for this scheme, which further improves its hardware efficiency through techniques such as partial comparison, division lookup tables, and multi-way parallelism. On 8/16-bit quantized models, our solution achieves a compression ratio of 2.76×~ 4.64×, outperforming existing approaches.