20 results for “Approximate caching”
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Shuheng Gao, Yuankai Fan, Jiaqiang Ma, Jiawei Shao +2 more
VisMemo is a serving system that enables approximate KV cache reuse for modern vision-language models to reduce prefill latency and memory overhead.
The paper characterizes the memory-rate trade-off for demand-private coded caching with heterogeneous file sizes and cache capacities.
This paper analyzes the effectiveness of caching heuristics on cloud object storage and provides a benchmark to measure their performance.
This paper introduces new metrics to evaluate semantic caching systems and shows that model selection is a calibration problem, not a ranking one.
The Sleator-Tarjan model used in competitive analysis of paging is incorrect, and this error undermines the performance predictions for online paging algorithms.
The paper proposes adaptive caching for key-value (KV) caching in large language models, improving cache hit rate and reducing time to first token.
ACRONYM is a novel algorithm-hardware co-designed platform that enables high-recall, continuous approximate nearest neighbor search in memory for dynamic vector databases, achieving massive throughput…
The paper presents a new method for privately releasing a k-dimensional vector under updates, improving the time complexity of sampling a noise vector from the binary tree mechanism with Gaussian nois…
Shubham Tiwari, Tapan Chugh, Nash Rickert, Simon Peter +2 more
This paper presents a new KVCache management layer called CacheWise that improves performance for coding agent workloads.
This paper proposes C^2, a set of techniques to improve the spatial locality of succinct tries, resulting in faster query performance and reduced memory footprint.
The paper proposes GroundedCache, an evidence-validated cache router that significantly improves the safety of reusing cached semantic answers in RAG systems by requiring multiple gates to validate th…
This paper proposes a Graph-based Weak Private Information Retrieval (G-WPIR) scheme for distributed storage systems with graph-based replication, identifying trade-offs between rate and privacy under…
This paper proposes a synopsis-guided approach to optimize vector search in databases by predicting the fidelity of different configurations and selecting the most cost-effective one for each query.
The paper proves a lower bound on the number of value-oracle queries required for a certain type of approximation algorithm.