Torchhd is a Python library for Hyperdimensional Computing and Vector Symbolic Architectures
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Updated
Jun 19, 2025 - Python
Torchhd is a Python library for Hyperdimensional Computing and Vector Symbolic Architectures
Local, gradient-free neuro-symbolic memory engine combining Hyperdimensional Computing (HDC/VSA), Hebbian plasticity, and graph triples for offline AI.
The Fuzzy-Pattern Tsetlin Machine library, with zero external dependencies, performs blazingly fast.
Hyperdimensional Computing Library for building Vector-Symbolic Architectures in Python 3
Cognitive Computing with Associative Memory
Accelerator for Hyperdimensional Computing (HDC)
Boolean Hypervectors with various operators for experiments in hyperdimensional computing (HDC).
Hyperdimensional computing in JAX, with statistical guarantees.
Local-first associative memory and vector search for AI agents, with Rust, Python, Node.js, and MCP integrations.
Intro to Hyperdimensional Computing (HDC) using a tea dataset
A computational foundation for AGI, based on hyperdimensional computing and framed in set theory.
A collection of Hyperdimensional Computing (HDC) models implemented in C++
A Rust library for hyperdimensional computing (HDC)
Symthaea: Consciousness-first cognitive architecture — HDC (16,384D) + CfC + IIT + FEP. Live portal: symthaea.luminousdynamics.io
HD3C: A lightweight classification framework designed for low-power devices.
An automated HDC platform
A chef's palate for AI agent memory; Un-mix any day's work into its exact projects, and detect workstreams nobody has named yet. Hyperdimensional fingerprints, zero dependencies.
Sema is a non-parametric, instance-based reasoning system: a Vector Symbolic Architecture over a content-addressable memory, with inference by weighted automated deduction.
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