147 citations · 604 across the 13 of their papers we have counts for
13 papers
A Survey on Hyperdimensional Computing aka Vector Symbolic Architectures, Part II: Applications, Cognitive Models, and Challenges
Denis Kleyko, Dmitri A. Rachkovskij, Evgeny Osipov +1
This is Part II of the two-part comprehensive survey devoted to a computing framework most commonly known under the names Hyperdimensional Computing and Vector Symbolic Architectur…
A Survey on Hyperdimensional Computing aka Vector Symbolic Architectures, Part I: Models and Data Transformations
Denis Kleyko, Dmitri A. Rachkovskij, Evgeny Osipov +1
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). B…
Hyperseed: Unsupervised Learning with Vector Symbolic Architectures
Evgeny Osipov, Sachin Kahawala, Dilantha Haputhanthri +4
Motivated by recent innovations in biologically-inspired neuromorphic hardware, this article presents a novel unsupervised machine learning algorithm named Hyperseed that draws on…
On Effects of Compression with Hyperdimensional Computing in Distributed Randomized Neural Networks
Antonello Rosato, Massimo Panella, Evgeny Osipov +1
A change of the prevalent supervised learning techniques is foreseeable in the near future: from the complex, computational expensive algorithms to more flexible and elementary tra…
Vector Symbolic Architectures as a Computing Framework for Emerging Hardware
Denis Kleyko, Mike Davies, E. Paxon Frady +8
This article reviews recent progress in the development of the computing framework vector symbolic architectures (VSA) (also known as hyperdimensional computing). This framework is…
Commentaries on "Learning Sensorimotor Control with Neuromorphic Sensors: Toward Hyperdimensional Active Perception" [Science Robotics Vol. 4 Issue 30 (2019) 1-10
Denis Kleyko, Ross W. Gayler, Evgeny Osipov
This correspondence comments on the findings reported in a recent Science Robotics article by Mitrokhin et al. [1]. The main goal of this commentary is to expand on some of the iss…
HyperEmbed: Tradeoffs Between Resources and Performance in NLP Tasks with Hyperdimensional Computing enabled Embedding of n-gram Statistics
Pedro Alonso, Kumar Shridhar, Denis Kleyko +2
Recent advances in Deep Learning have led to a significant performance increase on several NLP tasks, however, the models become more and more computationally demanding. Therefore,…
Density Encoding Enables Resource-Efficient Randomly Connected Neural Networks
Denis Kleyko, Mansour Kheffache, E. Paxon Frady +2
The deployment of machine learning algorithms on resource-constrained edge devices is an important challenge from both theoretical and applied points of view. In this article, we f…
Neural Distributed Autoassociative Memories: A Survey
V. I. Gritsenko, D. A. Rachkovskij, A. A. Frolov +3
Introduction. Neural network models of autoassociative, distributed memory allow storage and retrieval of many items (vectors) where the number of stored items can exceed the vecto…
Integer Echo State Networks: Efficient Reservoir Computing for Digital Hardware
Denis Kleyko, E. Paxon Frady, Mansour Kheffache +1
We propose an approximation of Echo State Networks (ESN) that can be efficiently implemented on digital hardware based on the mathematics of hyperdimensional computing. The reservo…
Autoscaling Bloom Filter: Controlling Trade-off Between True and False Positives
Denis Kleyko, Abbas Rahimi, Ross W. Gayler +1
A Bloom filter is a simple data structure supporting membership queries on a set. The standard Bloom filter does not support the delete operation, therefore, many applications use…
Holographic Graph Neuron: a Bio-Inspired Architecture for Pattern Processing
Denis Kleyko, Evgeny Osipov, Alexander Senior +2
This article proposes the use of Vector Symbolic Architectures for implementing Hierarchical Graph Neuron, an architecture for memorizing patterns of generic sensor stimuli. The ad…
An Approach for Self-Adaptive Path Loss Modeling for Accurate Positioning in Underground Environments
Evgeny Osipov, Denis Kleyko, Alexey Shapin
This paper proposes a real-time self-adaptive approach for accurate path loss estimation in underground mines or tunnels based on signal strength measurements from heterogeneous ra…