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.

翻译：超维计算（HD），亦称向量符号架构（VSA），是一种通过利用随机高维向量空间的特性，基于分布式表示进行计算的框架。科学界对这一跨学科领域的成果汇总与传播，始终是其发展的关键推动力。我们在此基础上推出Torchhd，这是一个面向HD/VSA的高性能开源Python库。Torchhd致力于提升HD/VSA的可及性，并为后续研究与应用开发提供高效基础。该易用库基于PyTorch构建，集成先进的HD/VSA功能、清晰文档及来自知名出版物的实现示例。对比现有公开代码与对应Torchhd实现的性能表明，实验运行速度最高可提升100倍。Torchhd可通过以下链接获取：https://github.com/hyperdimensional-computing/torchhd。