CrossKV: Accelerating Large Language Model Inference via Cross-Stage Dynamic Co-Optimization for KV Cache

The autoregressive nature of Large Language Models (LLMs) has enabled remarkable performance in language generation, making them a cornerstone in natural language processing. As context windows lengthen, the per-token key–value (KV) cache grows linearly with sequence length and turns the generation stage into a memory-bound operation. However, existing works focus primarily on an isolated stage of KV cache reduction, and most efforts incur significant hardware overheads. This results in a lack of cross-stage co-optimization and leaves significant reduction potential untapped. To address these challenges, we propose CrossKV, a software-hardware co-design architecture that accelerates LLM inference to fully achieve the potential of KV caching optimization. Motivated by our observation, we identify the three stages in KV caching and present a cross-stage co-optimization algorithm, including: a derivative-enhanced dynamic progressive pruning method, a DCT-driven key-vector low-rank compressing, and a dynamic clustered hybrid run-length encoding to reduce the KV caching while maintaining high accuracy. Then, an efficient architecture featuring cross-stage dynamic co-optimization with negligible hardware overhead is proposed to fully harness the algorithm of CrossKV. Our evaluations of CrossKV over 9 popular LLMs models and various long-context tasks demonstrate an average of$6.03\times $and$4.10\times $improvement for energy efficiency and speedup compared to existing SoTA Accelerators architecture. Compared to the Nvidia A100 GPU, CrossKV achieves an average$23.43\times $energy efficiency and$4.64\times $speedup, respectively.