Torchhd is a Python library for Hyperdimensional Computing and Vector Symbolic Architectures

The Fuzzy-Pattern Tsetlin Machine library, with zero external dependencies, performs blazingly fast.

Cognitive Computing with Associative Memory

Accelerator for Hyperdimensional Computing (HDC)

Hyperdimensional Computing Library for building Vector-Symbolic Architectures in Python 3

Boolean Hypervectors with various operators for experiments in hyperdimensional computing (HDC).

SWIR hyperspectral sensing for denied-environment ISR and materials discrimination. UAV-mounted 400-2500 nm imaging with edge-deployed CNN/SNN inference for contaminant detection and terrain/materials characterization in austere, offline environments.

Hyperdimensional computing with statistical guarantees: calibrated probabilities, conformal prediction sets, anomaly detection with a guaranteed false-positive rate

An automated HDC platform

A collection of Hyperdimensional Computing (HDC) models implemented in C++

Event based Vision Learning with HDC for Unmanned Systems

Nazgul is a unified C++/Python framework for time-optimal multi-joint trajectory planning (inherited from LongTermPlanner) and edge-efficient hyperdimensional computing (imported from Arthedain), providing a VSA backend factory with 8 algebras, HDC energy analysis at 45nm CMOS

Haskell-to-hardware compiler

A Rust library for hyperdimensional computing (HDC)

HDC-X: A Hyperdimensional Computing Framework for Efficient Classification on Low-Power Devices

A library for training and running HDC models on embedded devices.

Holographic vectors you can compute with. Bind structure, bundle sets, unbind components cross NumPy, PyTorch, and JAX.

Turn any neural network into a Vector Symbolic Architecture (VSA/HDC): NN-to-hypervector conversion, in-memory Hamming inference, HD-Glue model fusion, and FPGA/ASIC synthesis templates

An extended note on using Vector Symbolic Architectures to implement Next Generation Reservoir Computing