Torchhd：一个支持超维度计算与向量符号架构研究的开源Python库
Torchhd: An Open Source Python Library to Support Research on Hyperdimensional Computing and Vector Symbolic Architectures
AI总结：
本文介绍了Torchhd，一个基于PyTorch构建的高性能开源Python库，旨在降低超维度计算（HD）与向量符号架构（VSA）的使用门槛，为相关研究提供高效基础，其实验运行速度比现有公开代码快100倍。
摘要：
超维度计算（HD），也称为向量符号架构（VSA），是一种通过利用随机高维向量空间的特性来进行分布式表示计算的框架。科学界致力于汇集和传播这一多学科领域的研究，这对其发展至关重要。顺应这些努力，我们提出了Torchhd，这是一个用于HD/VSA的高性能开源Python库。Torchhd旨在使HD/VSA更易于访问，并作为进一步研究和应用开发的高效基础。这个易于使用的库基于PyTorch构建，具有最先进的HD/VSA功能、清晰的文档以及来自知名出版物的实现示例。将公开可用的代码与其相应的Torchhd实现进行比较，表明实验运行速度可提高100倍。Torchhd可在以下网址获取：https://github.com/hyperdimensional-computing/torchhd。
英文摘要：
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.
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