Hyperdimensional Computing

What if we've been computing in the wrong number of dimensions?

Most neural networks operate in spaces of hundreds or thousands of dimensions. Hyperdimensional computing operates in 10,000 dimensions or more. And in those vast spaces, something magical happens: vectors become almost orthogonal to each other by default, noise becomes irrelevant, and computation becomes both incredibly efficient and remarkably robust.

This isn't just scaling up. It's recognizing that high-dimensional spaces have bizarre, counterintuitive properties that make them perfect for brain-like computation. Properties that evolution discovered billions of years ago and that we're only now learning to exploit in silicon.

Why This Matters for Coherence

Brains don't store memories as precise addresses. They use distributed, overlapping representations that degrade gracefully and compose naturally. Hyperdimensional computing provides a mathematical framework for exactly this kind of organization: representations that maintain coherence through noise, that compose through simple operations, and that scale without centralized coordination.

Understanding hyperdimensional computing illuminates how coherence can be maintained through distributed, high-dimensional representations—principles that likely govern both biological and artificial intelligence.

What This Series Covers

This series explores hyperdimensional computing and vector symbolic architectures as an alternative paradigm for AI and cognitive modeling. We'll examine:

• The intellectual origins from Pentti Kanerva's work on sparse distributed memory
• Core operations: binding, bundling, and permutation in high dimensions
• Why high-dimensional spaces have "magical" geometric properties
• How HDC beats transformers on efficiency for certain tasks
• Industry adoption from Intel and IBM
• Applications to cognitive architectures and memory models
• Connections between HDC and active inference
• What hyperdimensional representations teach us about efficient coherence

By the end of this series, you'll understand why the question "How many dimensions should we compute in?" has a surprising answer—and why that answer might be closer to how brains actually work.

Articles in This Series

Computing in 10000 Dimensions: The Hyperdimensional Revolution

Introduction to hyperdimensional computing—why high-dimensional vectors might be how brains actually compute.

IdeasthesiaRyan Collison

Pentti Kanerva and the Origins of Hyperdimensional Computing

The intellectual history—how Kanerva's work on sparse distributed memory led to modern HDC.

IdeasthesiaRyan Collison

The Algebra of Hypervectors: Binding Bundling and Permutation

Core operations in hyperdimensional computing—how high-dimensional vectors compose and decompose.

IdeasthesiaRyan Collison

Why High Dimensions Are Magic: The Geometry of Hypervectors

The surprising mathematical properties of high-dimensional spaces that make HDC work.

IdeasthesiaRyan Collison

Hyperdimensional Computing Beats Transformers (On Edge Devices)

Benchmarking HDC against neural networks—where hyperdimensional approaches win on efficiency.

IdeasthesiaRyan Collison

Intel and IBM Bet on Hyperdimensional: Industry Applications

How major tech companies are investing in hyperdimensional computing—current and planned hardware.

IdeasthesiaRyan Collison

Hyperdimensional Computing for Cognitive Architectures

How HDC provides a substrate for cognitive modeling—connecting to theories of human memory and reasoning.

IdeasthesiaRyan Collison

Where Hyperdimensional Meets Active Inference: Efficient Coherence Computation

Bridging HDC to active inference—how hyperdimensional representations might implement FEP efficiently.

IdeasthesiaRyan Collison

Synthesis: What Hyperdimensional Computing Teaches About Efficient Coherence

Integration showing how HDC insights illuminate questions about how biological systems achieve coherent computation efficiently.

IdeasthesiaRyan Collison