Codeword Decomposition and Recoding Based Lossless Model Compression Algorithm and Its VLSI Decompressor Design - Ho, Lyu-Ming; Liang, Chih-Yao; Huang, Juinn-Dar (IEEE International Conference on Artificial Intelligence Circuits and Systems (Online), 2025)
Today, generative AI models, including large language models, are extensively used in various applications demanding real-time execution and substantial memory capacity, which presents a big challenge in achieving low-power and efficient implementations. Consequently, there is a growing need for model compression techniques for model size reduction. This work presents a new hardware-friendly, lossless model compression algorithm and its VLSI decompressor design to address the issue of limited memory resources. Our approach consists of two key ideas: codeword decomposition and recoding. The codeword decomposition method significantly reduces the complexity of the decompressor and thus increases the speed. The recoding method makes the frequency distribution of subwords more uneven, thus improving the compression rate. In addition, we propose a high-speed decompressor architecture design and its efficient hardware implementation. The proposed lossless compression algorithm significantly reduces the model size - 41% and 52% for Llama2-7B and BLOOM-7B, respectively. Moreover, multiple decompressors can be deployed in parallel to boost the overall throughput. Therefore, the proposed lossless compression algorithm and its decompressor design can effectively increase the runtime efficiency of memory-hungry generative AI models.