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SemantiCache: Efficient KV Cache Compression via Semantic Chunking and Clustered Merging

arXiv:2603.14303v1 Announce Type: new Abstract: Existing KV cache compression methods generally operate on discrete tokens or non-semantic chunks. However, such approaches often lead to semantic fragmentation, where linguistically coherent units are disrupted, causing irreversible information loss and degradation in model performance. To address this, we introduce SemantiCache, a novel compression framework that preserves semantic integrity by aligning the compression process with the semantic hierarchical nature of language. Specifically, we first partition the cache into semantically coherent chunks by delimiters, which are natural semantic boundaries. Within each chunk, we introduce a computationally efficient Greedy Seed-Based Clustering (GSC) algorithm to group tokens into semantic clusters. These clusters are further merged into semantic cores, enhanced by a Proportional Attention mechanism that rebalances the reduced attention contributions of the merged tokens. Extensive exper

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Shunlong Wu, Hai Lin, Shaoshen Chen, Tingwei Lu, Yongqin Zeng, Shaoxiong Zhan, Hai-Tao Zheng, Hong-Gee Kim · · 1 min read · 9 views
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arXiv:2603.14303v1 Announce Type: new Abstract: Existing KV cache compression methods generally operate on discrete tokens or non-semantic chunks. However, such approaches often lead to semantic fragmentation, where linguistically coherent units are disrupted, causing irreversible information loss and degradation in model performance. To address this, we introduce SemantiCache, a novel compression framework that preserves semantic integrity by aligning the compression process with the semantic hierarchical nature of language. Specifically, we first partition the cache into semantically coherent chunks by delimiters, which are natural semantic boundaries. Within each chunk, we introduce a computationally efficient Greedy Seed-Based Clustering (GSC) algorithm to group tokens into semantic clusters. These clusters are further merged into semantic cores, enhanced by a Proportional Attention mechanism that rebalances the reduced attention contributions of the merged tokens. Extensive experiments across diverse benchmarks and models demonstrate that SemantiCache accelerates the decoding stage of inference by up to 2.61 times and substantially reduces memory footprint, while maintaining performance comparable to the original model.

Executive Summary

The article 'SemantiCache: Efficient KV Cache Compression via Semantic Chunking and Clustered Merging' introduces a novel compression framework, SemantiCache, that addresses the issue of semantic fragmentation in existing KV cache compression methods. By aligning the compression process with the semantic hierarchical nature of language, SemantiCache preserves semantic integrity and accelerates the decoding stage of inference by up to 2.61 times, while maintaining performance comparable to the original model. The framework uses a combination of chunking, clustering, and merging techniques, along with a Proportional Attention mechanism to rebalance the reduced attention contributions of merged tokens. The authors demonstrate the effectiveness of SemantiCache through extensive experiments across diverse benchmarks and models.

Key Points

  • ▸ SemantiCache is a novel compression framework that preserves semantic integrity in KV cache compression methods.
  • ▸ The framework uses a combination of chunking, clustering, and merging techniques to align the compression process with the semantic hierarchical nature of language.
  • ▸ SemantiCache accelerates the decoding stage of inference by up to 2.61 times and substantially reduces memory footprint, while maintaining performance comparable to the original model.

Merits

Strength in addressing semantic fragmentation

SemantiCache effectively addresses the issue of semantic fragmentation in existing KV cache compression methods, which often lead to irreversible information loss and degradation in model performance.

Efficient compression through chunking, clustering, and merging

The combination of chunking, clustering, and merging techniques used in SemantiCache enables efficient compression of KV caches, while preserving semantic integrity.

Improved inference speed and reduced memory footprint

SemantiCache accelerates the decoding stage of inference by up to 2.61 times and substantially reduces memory footprint, making it a valuable addition to existing models.

Demerits

Limited evaluation on real-world applications

The authors' evaluation of SemantiCache is primarily based on experiments across diverse benchmarks and models, and it would be beneficial to assess its performance in real-world applications.

Potential complexity in implementation

The combination of chunking, clustering, and merging techniques used in SemantiCache may introduce complexity in implementation, which could be a barrier to adoption.

Expert Commentary

The introduction of SemantiCache is a significant contribution to the field of AI and machine learning, as it addresses a critical issue in existing compression methods and provides an efficient compression framework for large-scale models. The combination of chunking, clustering, and merging techniques used in SemantiCache is innovative and effective in preserving semantic integrity. However, the potential complexity in implementation and the limited evaluation on real-world applications are areas that require further attention. Overall, SemantiCache has the potential to significantly impact the field of AI and machine learning, and it is essential to continue evaluating and refining its performance.

Recommendations

  • ✓ Further evaluation of SemantiCache on real-world applications to assess its performance and scalability.
  • ✓ Investigation into the potential complexity in implementation and development of more efficient and user-friendly implementation methods.

Sources

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