This two-part comprehensive survey is devoted to a computing framework most commonly known under the names Hyperdimensional Computing and Vector Symbolic Architectures (HDC/VSA). Both names refer to a family of computational models that use high-dimensional distributed representations and rely on the algebraic properties of their key operations to incorporate the advantages of structured symbolic representations and vector distributed representations. Notable models in the HDC/VSA family are Tensor Product Representations, Holographic Reduced Representations, Multiply-Add-Permute, Binary Spatter Codes, and Sparse Binary Distributed Representations but there are other models too. HDC/VSA is a highly interdisciplinary field with connections to computer science, electrical engineering, artificial intelligence, mathematics, and cognitive science. This fact makes it challenging to create a thorough overview of the field. However, due to a surge of new researchers joining the field in recent years, the necessity for a comprehensive survey of the field has become extremely important. Therefore, amongst other aspects of the field, this Part I surveys important aspects such as: known computational models of HDC/VSA and transformations of various input data types to high-dimensional distributed representations. Part II of this survey is devoted to applications, cognitive computing and architectures, as well as directions for future work. The survey is written to be useful for both newcomers and practitioners.

翻译：本两卷综合综述致力于一种常被称为超维度计算与向量符号架构（HDC/VSA）的计算框架。该名称指代一类使用高维分布式表征的计算模型，其核心运算的代数性质融合了结构化符号表征与向量分布式表征的优势。HDC/VSA家族的典型模型包括张量积表征、全息约化表征、乘-加-置换、二元散列编码及稀疏二元分布式表征，此外还存在其他模型。HDC/VSA是一个高度跨学科的研究领域，涉及计算机科学、电气工程、人工智能、数学与认知科学，这一特性使得对该领域进行全面概述颇具挑战。然而，近年新研究者大量涌入该领域，亟需系统性综述。因此，本第一部分聚焦该领域重要议题：HDC/VSA的已知计算模型，以及各类输入数据类型到高维分布式表征的变换方法。本综述第二部分将涵盖应用、认知计算与架构及未来研究方向。本文旨在为新手与从业者提供实用参考。