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AI Research
arXiv CS.AI·May 28·8 min read
Clark Hash: Stateless Sparse Johnson-Lindenstrauss Quantization for Neural Embeddings
TL;DR
Neural embeddings (vector representations of data) are memory-intensive and slow to retrieve at scale. Clark Hash applies Johnson-Lindenstrauss quantization—a mathematical technique that compresses high-dimensional vectors while preserving distances—in a stateless, sparse manner requiring no stored state.
✦ Why It Matters
Engineers can deploy smaller, faster embedding systems without retraining models or maintaining stateful compression infrastructure.
Key Takeaways
- Deploy Clark Hash for on-the-fly embedding compression without training or state management overhead.
- Reduce embedding storage and retrieval latency while preserving vector similarity guarantees mathematically.
- Use stateless hashing to compress embeddings deterministically at inference time without lookup tables.
- Evaluate sparse quantization trade-offs between compression ratio and downstream task accuracy for your use case.
Full Summary
Neural embeddings represent text, images, or other data as vectors in high-dimensional space, enabling similarity search and machine learning tasks. However, storing and retrieving millions of embeddings is computationally expensive.
Clark Hash addresses this by applying Johnson-Lindenstrauss quantization, a dimensionality reduction technique that mathematically guarantees distance preservation between vectors even after compression. The approach is stateless (requires no persistent lookup tables or training) and sparse (uses only a subset of dimensions), making it lightweight and deployable without infrastructure overhead.
The method hashes embeddings deterministically, enabling on-the-fly compression during inference. Results demonstrate reduced memory footprint and faster retrieval times compared to full-precision embeddings, with minimal loss in downstream task accuracy.