论文标题
TORCHHD：一个开源Python库，用于支持有关高维计算和矢量符号体系结构的研究
Torchhd: An Open Source Python Library to Support Research on Hyperdimensional Computing and Vector Symbolic Architectures
论文作者
论文摘要
高维计算（HD），也称为矢量符号体系结构（VSA），是通过利用随机高维矢量空间的属性来计算分布式表示的框架。科学界对在这个尤其多学科领域进行汇总和传播研究的承诺对于其发展而言至关重要。加入这些努力，我们介绍了Torchhd，这是一个高性能的HD/VSA的高性能开源Python库。 Torchhd试图使高清/VSA更容易访问，并为进一步的研究和应用程序开发提供了有效的基础。易于使用的库建立在Pytorch之上，并具有最先进的HD/VSA功能，清晰的文档和来自知名出版物的实施示例。将公开可用的代码与相应的TorchHD实现进行比较表明，实验可以快速运行速度高达100倍。 Torchhd可在以下网址提供：https：//github.com/hyperdimensional-computing/torchhd。
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.
