13 citations · 20 across the 6 of their papers we have counts for
7 papers
Computing with Residue Numbers in High-Dimensional Representation
Christopher J. Kymn, Denis Kleyko, E. Paxon Frady +4
We introduce Residue Hyperdimensional Computing, a computing framework that unifies residue number systems with an algebra defined over random, high-dimensional vectors. We show ho…
Efficient Decoding of Compositional Structure in Holistic Representations
Denis Kleyko, Connor Bybee, Ping-Chen Huang +4
We investigate the task of retrieving information from compositional distributed representations formed by Hyperdimensional Computing/Vector Symbolic Architectures and present nove…
Efficient Optimization with Higher-Order Ising Machines
Connor Bybee, Denis Kleyko, Dmitri E. Nikonov +3
A prominent approach to solving combinatorial optimization problems on parallel hardware is Ising machines, i.e., hardware implementations of networks of interacting binary spin va…
Cross-Frequency Coupling Increases Memory Capacity in Oscillatory Neural Networks
Connor Bybee, Alexander Belsten, Friedrich T. Sommer
An open problem in neuroscience is to explain the functional role of oscillations in neural networks, contributing, for example, to perception, attention, and memory. Cross-frequen…
Deep Learning in Spiking Phasor Neural Networks
Connor Bybee, E. Paxon Frady, Friedrich T. Sommer
Spiking Neural Networks (SNNs) have attracted the attention of the deep learning community for use in low-latency, low-power neuromorphic hardware, as well as models for understand…
Integer Factorization with Compositional Distributed Representations
Denis Kleyko, Connor Bybee, Christopher J. Kymn +5
In this paper, we present an approach to integer factorization using distributed representations formed with Vector Symbolic Architectures. The approach formulates integer factoriz…
NxTF: An API and Compiler for Deep Spiking Neural Networks on Intel Loihi
Bodo Rueckauer, Connor Bybee, Ralf Goettsche +3
Spiking Neural Networks (SNNs) are a promising paradigm for efficient event-driven processing of spatio-temporally sparse data streams. SNNs have inspired the design and can take a…