Efficient Hyperdimensional Computing with Direct Memory Access and PCA for Knowledge Graphs

1. Introduction

Data-intensive applications such as natural language processing (NLP), knowledge graphs, and real-time recommendation systems often encounter limitations in memory and retrieval speed. Combining Hyperdimensional Computing (HDC), Direct Memory Access (DMA) and Principal Component Analysis (PCA) this system provides an optimized solution for large-scale storage and querying needs. We’ll explore each component’s technical underpinnings and see how, together, they create an efficient scalable knowledge graph capable of handling millions of relationships.

2. Theoretical Background

2.1 Hyperdimensional Computing (HDC): An Overview

Hyperdimensional Computing (HDC) mimics certain cognitive processes by encoding information as high-dimensional vectors, or hypervectors, typically with 10,000 or more dimensions. This high-dimensionality, inspired by properties of biological systems, offers:

• Error Tolerance: Noise resilience due to distributed representations.
• Associative Properties: Capability for rapid, parallel search and pattern recognition.
• Binding and Bundling: Operations…