HDC Vector Operations Maintain Accuracy at Scale, Unlike LLM-Based RAG Systems

Oct 3

3 min read

In response to this article: Do Vector Databases Lose Accuracy at Scale?

The recent study by EyeLevel.ai highlighting accuracy degradation in vector-based Retrieval Augmented Generation (RAG) systems for Large Language Models (LLMs) has raised concerns about the scalability of vector search technologies. However, it's crucial to understand that these findings do not necessarily apply to all vector-based operations, particularly those used in Hyperdimensional Computing (HDC) architectures.

What is Hyperdimensional Computing (HDC)?

HDC, a brain-inspired computing paradigm, operates on fundamentally different principles compared to traditional vector databases used in RAG systems. While RAG systems for LLMs may experience performance degradation as the number of documents increases, HDC demonstrates remarkable resilience and maintains accuracy even at scale.

HDC, a brain-inspired computing paradigm, operates on fundamentally different principles compared to traditional vector databases used in RAG systems.

Advantages to HDC

One of the key strengths of HDC is its inherent robustness to noise and errors. HDC uses high-dimensional vectors, typically with thousands of dimensions, to represent information. This high dimensionality provides HDC with unique properties that set it apart from conventional vector search methods. Even if a hypervector suffers a significant numbers of random bit flips, it remains close to the original vector. This error tolerance is at least 10 times greater than that of traditional artificial neural networks, which are already more resilient than conventional computing architectures.

"Even if a hypervector suffers significant numbers of random bit flips, it remains close to the original vector. This error tolerance is at least 10 times greater than that of traditional artificial neural networks, which are already more resilient than conventional computing architectures."

Efficient and Robust Encoding

HDC's distributed representations allow for more efficient and robust information encoding. Each concept in HDC is represented across many dimensions rather than in a single location. This distributed nature potentially mitigates the issues of accuracy loss seen in traditional vector databases as they scale.

The computational efficiency of HDC is another factor contributing to its ability to maintain accuracy at scale. HDC performs computations directly in high-dimensional space using simple operations like element-wise additions and dot products. This approach can be more computationally efficient than traditional vector search methods, potentially allowing for better scaling without significant loss in accuracy.

Accuracy - Even at Large Scale

Furthermore, HDC offers a flexible framework for representing and manipulating complex data structures and relationships. This flexibility could allow for more sophisticated encoding of information compared to traditional vector embeddings, potentially maintaining accuracy even as the scale increases.

The high-dimensional space used in HDC allows for a vast number of nearly orthogonal vectors. This property could potentially address some of the challenges faced by traditional vector databases in maintaining distinct representations as the number of entries grows.

"The high-dimensional space used in HDC allows for a vast number of nearly orthogonal vectors. This property could potentially address some of the challenges faced by traditional vector databases in maintaining distinct representations as the number of entries grows."

Recent research has shown that HDC models can allow up to 47.6% energy saving on associative memory with a negligible accuracy loss (≤1%). This demonstrates the robustness of HDC models to hardware errors and their ability to maintain accuracy even under aggressive voltage scaling.

Moreover, studies have explored low-cost error mitigation mechanisms for HDC, such as detecting and masking corrupted words or bits. These mechanisms can effectively improve the resilience of HDC by up to 10,000 times, further enhancing its ability to maintain accuracy at scale.

No Need for LLMs

It's important to note that HDC is not typically run on LLMs, which are the focus of the EyeLevel.ai study. Instead, HDC is often implemented on specialized hardware or neuromorphic computing systems, which are designed to leverage its unique properties.

HDC hypervectors are not typically run on LLMs.

Conclusion

In conclusion, while vector-based RAG systems for LLMs may face challenges in maintaining accuracy at scale, HDC demonstrates remarkable resilience and accuracy preservation. Its unique properties, including high dimensionality, distributed representations, and inherent error tolerance, make it a promising technology for scalable, accurate vector-based operations.

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