A Hybrid-Structured Lossless Compression–Decompression Engine for Intermediate Feature Maps in Vision Neural Networks

With the continuous evolution of vision neural networks, the off-chip transmission and storage of intermediate feature maps have become a major bottleneck during inference, especially in resource-constrained edge devices. Lossless compression of the intermediate feature maps exhibits the plug-and-play feature without requiring additional evaluation or retraining. However, previous lossless compression hardware engines face challenges in the tradeoff between hardware complexity and compression ratio. To address this issue, this article proposes a hybrid-structured lossless compression and decompression engine of intermediate feature maps in vision neural networks. The proposed work combines multidimensional prediction, run-length encoding (RLE), and extended encoding (EE). In the predictive stage, delta and contextual prediction are employed to enhance sparsity for compression efficiency. In the encoding stage, RLE minimizes redundancy between adjacent bytes, and EE is used to select various compression methods to decrease bit-level redundancy dynamically. The average compression ratio of widely used vision neural networks is 48.42%, which is better than that of Huffman coding. Compared with the state-of-the-art works, it improves the average compression ratio by 20.90%. For hardware implementation, hardware reuse and data reuse are employed to achieve 17.95% and 21.18% reduction of the gate count and the power, respectively. Experimental results show that we achieve a throughput-per-area of 1.10 [(bits/cycle)/K GCs] and a throughput-per-power of 1.19 [(bits/cycle)/mW] in the 28-nm process node.