  Hyperdimensional computing (HD), also known as vector symbolic architectures
(VSA), is a framework for computing with distributed representations by
exploiting properties of random high-dimensional vector spaces. The commitment
of the scientific community to aggregate and disseminate research in this
particularly multidisciplinary area has been fundamental for its advancement.
Joining these efforts, we present Torchhd, a high-performance open source
Python library for HD/VSA. Torchhd seeks to make HD/VSA more accessible and
serves as an efficient foundation for further research and application
development. The easy-to-use library builds on top of PyTorch and features
state-of-the-art HD/VSA functionality, clear documentation, and implementation
examples from well-known publications. Comparing publicly available code with
their corresponding Torchhd implementation shows that experiments can run up to
100x faster. Torchhd is available at:
https://github.com/hyperdimensional-computing/torchhd.
