Hyperdimensional Computing and Vector Symbolic Architectures

This Collection supports and amplifies research related to SDG 9 - Industry, Innovation and Infrastructure. 

Hyperdimensional Computing (HD), or Vector Symbolic Architectures (VSA), refers to a family of computational approaches that combine high-dimensional vector representations with a small set of algebraic operations to solve computational problems. The ideas underlying HD/VSA emerged in the late 1980s and early 1990s, driven by the pioneering work of P. Kanerva, E. Kussul, E. Mizraji, T. Plate, D. Rachkovskij, and P. Smolensky. Among these efforts, T. Plate’s Holographic Reduced Representations were particularly influential within the machine learning community. HD/VSA is a growing area lying at the intersection of several disciplines, including computer science, artificial intelligence and machine learning (AI/ML), mathematics, electrical engineering, neuroscience, and cognitive science.

Departing from conventional scalar-number-based computing, HD/VSA defines computations at the level of very large populations of neurons that are represented as distributed, high-dimensional vectors. These representations, together with algebraic operations, enable computing in superposition and can provide elegant solutions to long-standing challenges in AI/ML, such as the variable binding problem: how to represent and manipulate relationships between variables in a flexible yet precise manner. The inherent robustness of distributed vector representations makes HD/VSA compatible with emerging types of stochastic hardware. Its algebraic operations allow representing and manipulating fundamental data structures in a way that is both compact and scalable. This makes it well-suited for reducing computational costs of existing AI/ML systems and for use in settings like resource-constrained devices or mobile robots, where traditional solutions might be too costly or power-hungry. Additionally, HD/VSA complements and integrates well with neural networks. Recent work in the area has shown promising results across various domains, suggesting that this computational paradigm could play a key role in the development of next-generation AI/ML systems.

This collection invites contributions reporting both theoretical and practical advances in HD/VSA, particularly those aligned with the broad theme of unconventional computing. Topics of special interest include:

1. Intersections between HD/VSA and other unconventional computing paradigms, such as coupled oscillations, cellular automata, reservoir computing, neuromorphic computing, dynamic neural fields, and others.
2. Novel instances of the HD/VSA family or their adaptations leading to desired computational properties.
3. Novel algorithms that leverage the core principles of HD/VSA and potentially capture interesting properties of neural circuits in biological systems.
4. Hardware implementations of HD/VSA systems and algorithms, particularly those using emerging computing hardware.
5. Hybrid systems combining HD/VSA with other AI/ML approaches, including deep learning, kernel methods, neuro-symbolic AI, and others.
6. Innovative applications of HD/VSA, supported by rigorous evaluation, benchmarking (where applicable), and comparison to state-of-the-art methods.