Special Issue "High Performance Reconfigurable Computing"

A special issue of Algorithms (ISSN 1999-4893).

Deadline for manuscript submissions: closed (15 June 2019)

Special Issue Editor

Guest Editor
Dr. Seyong Lee

Future Technologies Group, Oak Ridge National Laboratory, Oak Ridge, TN, USA
Website | E-Mail
Interests: high performance computing; compiler; parallel programming; heterogeneous computing

Special Issue Information

Dear Colleagues,

Reconfigurable computing using Field Programmable Gate Arrays (FPGAs) and coarse-grained reconfigurable devices have received renewed interest because of their unique combination of flexibility, performance, and energy efficiency. Their reconfigurable nature allows these architectures to be customized to match the needs of a given application so that they can achieve much higher energy efficiency and/or performance gains compared to conventional CPUs and GPUs.

Recent trends in technologies, such as (1) high-end FPGAs with hardened floating-point data signal processing blocks, (2) new system-on-chip devices integrating CPUs, FPGAs, and GPUs, and (3) high-level programming support (HLS, OpenCL, OpenACC, etc.), make high performance reconfigurable computing more attractive for serious exploration in scientific simulation and data analytics.

We invite you to submit your latest research on the theoretical and practical issues in applying reconfigurable computing to high performance computing (HPC) and data analytics.  Potential topics of this Special Issue include, but are not limited to:

  • HPC and machine learning algorithms and applications implemented on reconfigurable devices
  • Programming models, compilers, and system software for reconfigurable computing
  • Algorithms and methods for leveraging dynamic reconfiguration to increase performance, flexibility, reliability, or programmability.
  • Mapping algorithms and tools for heterogeneous system-on-chip devices

Dr. Seyong Lee
Guest Editor

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All papers will be peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Algorithms is an international peer-reviewed open access monthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 1000 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • High performance computing
  • Reconfigurable computing
  • Data analytics

Published Papers (1 paper)

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Research

Open AccessArticle
A New Method of Applying Data Engine Technology to Realize Neural Network Control
Algorithms 2019, 12(5), 97; https://doi.org/10.3390/a12050097
Received: 25 March 2019 / Revised: 26 April 2019 / Accepted: 5 May 2019 / Published: 9 May 2019
PDF Full-text (7922 KB) | HTML Full-text | XML Full-text
Abstract
This paper presents a novel diagonal recurrent neural network hybrid controller based on the shared memory of real-time database structure. The controller uses Data Engine (DE) technology, through the establishment of a unified and standardized software architecture and real-time database in different control [...] Read more.
This paper presents a novel diagonal recurrent neural network hybrid controller based on the shared memory of real-time database structure. The controller uses Data Engine (DE) technology, through the establishment of a unified and standardized software architecture and real-time database in different control stations, effectively solves many problems caused by technical standard, communication protocol, and programming language in actual industrial application: the advanced control algorithm and control system co-debugging difficulties, algorithm implementation and update inefficiency, and high development and operation and maintenance costs effectively fill the current technical gap. More importantly, the control algorithm development uses a unified visual graphics configuration programming environment, effectively solving the problem of integrated control of heterogeneous devices; and has the advantages of intuitive configuration and transparent data processing process, reducing the difficulty of the advanced control algorithms debugging in engineering applications. In this paper, the application of a neural network hybrid controller based on DE in motor speed measurement and control system shows that the system has excellent control characteristics and anti-disturbance ability, and provides an integrated method for neural network control algorithm in a practical industrial control system, which is the major contribution of this article. Full article
(This article belongs to the Special Issue High Performance Reconfigurable Computing)
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