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About this paper
Jungwoo Kim, Minsang Kim, Jaeheon Lee, Chanwoo Moon, Heejin Kim, Taeho Hwang, Woosuk Chung, Yeseong Kim and 1 more
Researchers from DGIST, POSTECH, and SK Hynix developed SISO, a semantic caching system that significantly improves LLM serving efficiency and reliability. It achieves higher cache hit ratios by 43% on average, 1.7x faster cache lookups, and better Service Level Objective attainment by adapting to workload intensity, while maintaining comparable response quality.
Key insights
- Traditional caching strategies are inefficient for LLMs because individual semantically similar queries lead to redundant cache entries and short-window eviction policies fail to capture long-term "semantic locality."
- Aggregating semantically similar queries into centroids and using a replacement policy based on centroid cluster_size (semantic density) and access_count can significantly improve cache hit rates and memory efficiency.
- Dynamically adjusting the semantic similarity retrieval threshold based on the system's workload and predicted waiting times is crucial for balancing output quality and latency, enabling better SLO attainment.
Summary by alphaXiv.
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