Hyperdimensional Computing
Today's AI systems are powerful, but opaque. We're working on building the alternative: very high-dimensional representations of connected data that composably encode meaning, and are explainable and robust to noise, by design. Allow your agents to associate, reason, learning from a handful of examples.
What is HDC?
Hyperdimensional computing (HDC) represents a concept (a phrase, a number, a list, or a whole record) as high-dimensional "hypervectors" with thousands of dimensions, and provides an elegant, yet simple algebra to operate over these hypervectors. Any two hypervectors drawn at random are near-orthogonal, so you can encode rich information in a single hypervector. The three operations below are the core of HDC's algebra.
Adding hypervectors together where the sum remains similar to each of its inputs. A point, record, or context becomes a single hypervector that can be stored, retrieved, and compared to other hypervectors.
Multiply two hypervectors and the result is dissimilar to either input. The operation is its own inverse, so binding a role to a value makes a pair that can be unbound to recover the originals.
Rotate a hypervector's components, making it dissimilar to the original. Apply it n times and you have an encoded position, allowing sequences and paths to live in the same space as everything else.
Approach
Most of the value in this field is still upstream of the implementation － a lot of the work in HDC lies in building the right encoder. This is an active area of research. Once we arrive at the right representation for the data at hand, we can then efficiently run downstream tasks like associative search and online learning.
In very high-dimensional space, random hypervectors are nearly orthogonal and information distributes holographically. Even if you degrade a third of a hypervector, it still doesn't lose its entire information content.
The algebra exposes reversible operations. A bound hypervector representation can be unbound. When an agent arrives at a result, we can unpack which components contributed the most by inspecting the algebra and applying its operators.
Binding and bundling are elementwise operations over real, binary or bipolar hypervectors. The same reasoning that needs a GPU cluster in traditional approaches can run on commodity hardware in HDC.
We actively write up what we learn. Our blog contains valuable learning material on our research and benchmarks, so any claims are backed by tangible evidence on realistic datasets.
Blog
We publish new blogs as we discover new insights. If you want to know how HDC works in practice, start here.
Hyperdimensional Computing (HDC) leverages hypervectors—high-dimensional representations that store and process information holographically. This unique property allows hypervectors to be robust to noise and loss, making them powerful for AI, neuromorphic computing, and cognitive models. This article explores what 'holographic' means in the context of hypervectors and why it matters.
ReadHypervectors are the foundation of Hyperdimensional Computing (HDC), representing data in ultra-high-dimensional spaces. They enable robust, efficient, and brain-like computation, making them a powerful tool for AI, machine learning, and neuromorphic computing. This article explores the nature of hypervectors, their properties, and how they are used in HDC.
ReadHyperdimensional Computing (HDC) is an emerging paradigm inspired by brain-like processing. It offers a powerful way to encode, manipulate, and reason about information using high-dimensional vectors, enabling robust and efficient AI applications. This article introduces the key concepts, motivations, and potential of HDC.
ReadContact
Agents that return unreliable results are usually hitting a representation problem, not a prompting one. Associative search and memory can surface plausible patterns within the data that keyword and embedding similarity both miss. HDC systems can learn online, from a handful of examples, rather than needing to retrain a model from scratch. If these problems resonate with you, we'd love to hear more.
We usually reply within two business days.
