18 research outputs found
Community detection with spiking neural networks for neuromorphic hardware
We present results related to the performance of an algorithm for community
detection which incorporates event-driven computation. We define a mapping
which takes a graph G to a system of spiking neurons. Using a fully connected
spiking neuron system, with both inhibitory and excitatory synaptic
connections, the firing patterns of neurons within the same community can be
distinguished from firing patterns of neurons in different communities. On a
random graph with 128 vertices and known community structure we show that by
using binary decoding and a Hamming-distance based metric, individual
communities can be identified from spike train similarities. Using bipolar
decoding and finite rate thresholding, we verify that inhibitory connections
prevent the spread of spiking patterns.Comment: Conference paper presented at ORNL Neuromorphic Workshop 2017, 7
pages, 6 figure
Acoustic Source Localization via Time Difference of Arrival Estimation for Distributed Sensor Networks Using Tera-Scale Optical Core Devices
For real-time acoustic source localization applications, one of the primary challenges is the considerable
growth in computational complexity associated with the emergence of ever larger, active
or passive, distributed sensor networks. These sensors rely heavily on battery-operated system
components to achieve highly functional automation in signal and information processing. In order
to keep communication requirements minimal, it is desirable to perform as much processing on
the receiver platforms as possible. However, the complexity of the calculations needed to achieve
accurate source localization increases dramatically with the size of sensor arrays, resulting in substantial growth of computational requirements that cannot be readily met with standard hardware.
One option to meet this challenge builds upon the emergence of digital optical-core devices. The
objective of this work was to explore the implementation of key building block algorithms used in
underwater source localization on the optical-core digital processing platform recently introduced
by Lenslet Inc. This demonstration of considerably faster signal processing capability should be
of substantial significance to the design and innovation of future generations of distributed sensor
networks
Performance Models for Split-execution Computing Systems
Split-execution computing leverages the capabilities of multiple
computational models to solve problems, but splitting program execution across
different computational models incurs costs associated with the translation
between domains. We analyze the performance of a split-execution computing
system developed from conventional and quantum processing units (QPUs) by using
behavioral models that track resource usage. We focus on asymmetric processing
models built using conventional CPUs and a family of special-purpose QPUs that
employ quantum computing principles. Our performance models account for the
translation of a classical optimization problem into the physical
representation required by the quantum processor while also accounting for
hardware limitations and conventional processor speed and memory. We conclude
that the bottleneck in this split-execution computing system lies at the
quantum-classical interface and that the primary time cost is independent of
quantum processor behavior.Comment: Presented at 18th Workshop on Advances in Parallel and Distributed
Computational Models [APDCM2016] on 23 May 2016; 10 page
Quantum-well infrared photodetector structure synthesis: Methodology and experimental verification
© 2003 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.DOI: 10.1109/JQE.2002.80816
Analysis, design, and testing of semiconductor intersubband devices
Ph.D.Thomas K. Gaylor
Nanosystems, Edge Computing, and the Next Generation Computing Systems
It is widely recognized that nanoscience and nanotechnology and their subfields, such as nanophotonics, nanoelectronics, and nanomechanics, have had a tremendous impact on recent advances in sensing, imaging, and communication, with notable developments, including novel transistors and processor architectures. For example, in addition to being supremely fast, optical and photonic components and devices are capable of operating across multiple orders of magnitude length, power, and spectral scales, encompassing the range from macroscopic device sizes and kW energies to atomic domains and single-photon energies. The extreme versatility of the associated electromagnetic phenomena and applications, both classical and quantum, are therefore highly appealing to the rapidly evolving computing and communication realms, where innovations in both hardware and software are necessary to meet the growing speed and memory requirements. Development of all-optical components, photonic chips, interconnects, and processors will bring the speed of light, photon coherence properties, field confinement and enhancement, information-carrying capacity, and the broad spectrum of light into the high-performance computing, the internet of things, and industries related to cloud, fog, and recently edge computing. Conversely, owing to their extraordinary properties, 0D, 1D, and 2D materials are being explored as a physical basis for the next generation of logic components and processors. Carbon nanotubes, for example, have been recently used to create a new processor beyond proof of principle. These developments, in conjunction with neuromorphic and quantum computing, are envisioned to maintain the growth of computing power beyond the projected plateau for silicon technology. We survey the qualitative figures of merit of technologies of current interest for the next generation computing with an emphasis on edge computing