Juan Carlos González, Carlos Eduardo Silva, Fernando Alejandro Soto, 2026. "Hyper Dimensional Computing for Next-Generation Information Processing", International Journal of Review Computing and Information Technology (IJRCIT) 1(1): 37-50.

Growing interest in intelligent computing systems is revealing crucial shortcomings of traditional computing approaches. Abstract Modern applications including artificial intelligence (AI), Internet of Things (IoT), edge computing, autonomous systems and big data analytics tend to handle massive volume of complex heterogeneous data which must be processed efficiently. However, the bottlenecks of conventional von Neumann architecture such as high power consumption, memory limitation and non-scalability make this approach unsatisfactory for processing information in next-generation applications. In response, vector symbolic architecture (VSA) or hyper dimensional computing (HDC) has emerged as a new brain-inspired computing paradigm.
Hyper dimensional Computing encodes information as very high-dimensional vectors, known as hyper vectors, spanning thousands of dimensions. Such distributed representations resemble properties of neural information in the brain and leverage robust, scalable fault-tolerant computation. Unlike classical symbolic systems, HDC integrates symbolic and distributed memory representations into one framework, making it possible to encode complex highdimensional information while being easy and efficient in storage, retrieval, and manipulation. We can do this because we rely on very elementary arithmetic — bundling, binding and permutation and making neural net contrast with monetarism so that our learning and inference scale quickly while staying computationally cheap.
The research paper details an extensive study of Hyper dimensional Computing, and how it contributes to next generation information processing. This paper investigates the theoretical underpinnings of HDC, hyper vector representation methods, learning paradigms, memory architecture and hardware acceleration strategies. In addition, it discusses the convergence of HDC with other emerging technologies such as xeromorphic computing, edge intelligence and purpose-built hardware architectures. Different areas of applications like machine learning, natural language processing, healthcare monitoring, robotics, cyber-security and IoT systems are examined which show the practical benefits of HDC.Hyperdimensional Computing has showcased its potential for energy efficiency, noise robustness as well as ultra-parallel information processing capabilities. Overall, HDC has great potential as a technology for building future intelligent systems despite the challenges in standardization, scalability and theoretical understanding. These results indicate that Hyper dimensional Computing may turn out to be a core technology of 21st century computing platforms with the ability to adapt, behave consistently and execute efficiently in response to continuous growth of data-driven applications.

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