TRACE: A Fast Transformer-based General-Purpose Lossless Compressor
Yu Peng Mao, Yufei Cui, Tei‐Wei Kuo, Chun Jason Xue · Proceedings of the ACM Web Conference 2022 · 2022
Deep-learning-based compressor has received interests recently due to much improved compression ratio. However, modern approaches suffer from long execution time. To ease this problem, this paper targets on cutting down the execution time of deep-learning-based compressors. Building history-dependencies sequentially (e.g., recurrent neural networks) is responsible for long inference latency. Instead, we introduce transformer into deep learning compressors to build history-dependencies in parallel. However, existing transformer is too heavy in computation and incompatible to compression tasks.