HiKV: Hierarchical Importance-Aware KV Cache with Hardware Acceleration for LLM Decoding
With the rapid adoption of long-context large language models (LLMs), the continuously growing KV cache during decoding has become the critical memory bottleneck. To tackle this challenge, we propose HiKV, a novel algorithm-hardware co-design that exploits KV cache redundancy through hierarchical importance awareness. Algorithmically, HiKV compresses the KV cache at two granularities: Stage I evicts unimportant tokens within a fixed budget, and Stage II further loads only the significant elements of each retained token, reaching compression ratios unattainable at a single granularity. Architecturally, we develop a dedicated accelerator centered on a reconfigurable importance sorter that s...
| Authors | Chao Fang, Jun Yin, Man Shi et al. |
|---|---|
| Primary category | cs.AR |
| Published | 24 July 2026 |
| Last updated at source | 24 July 2026 |
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