Leveraging Hyperdimensional Computing: Building a Hypervector Database for Efficient Similarity Search

Introduction

Hyperdimensional computing (HDC) also known as vector-symbolic architectures (VSA) provides a framework for representing and manipulating data in high-dimensional spaces. It has shown tremendous promise in areas like brain-inspired computing, pattern recognition and fast similarity search due to its efficiency in handling high-dimensional data.

This blog walks through a practical implementation of HDC for similarity search using the Annoy library building in turn a hyper vector database. We will break down the code, explain its mathematical foundations, and illustrate use cases and results.

By the end of this article, you’ll have a clear understanding of how HDC is applied to different data types, how similarity search works in high-dimensional spaces and how this approach can be used for real-world applications like information retrieval, recommendation systems and large-scale data clustering.

Mathematical Theorem: High-Dimensional Representation and Similarity

The central idea in hyperdimensional computing is that objects or data points can be represented as hypervectors — vectors in high-dimensional…