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Recent Developments in Algorithms and Computational Complexity

A special issue of Applied Sciences (ISSN 2076-3417). This special issue belongs to the section "Computing and Artificial Intelligence".

Deadline for manuscript submissions: closed (20 June 2024) | Viewed by 1329

Special Issue Editor


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Guest Editor
School of Science, Harbin Institute of Technology, Shenzhen 518055, China
Interests: information; high dimensional data; topological data analysis; urban dynamics; q-analysis; integration entropy

Special Issue Information

Dear Colleagues,

Faced with the massive scale of data in the era of big data and artificial intelligence, higher requirements and challenges are being put forward in the field of applied science for the designed algorithm. This is particularly the case in relation to efforts to enhance the computational complexity of the designed algorithms, a factor which directly affects the solution efficiency. Although there are related studies on algorithms in the existing literature, publishing the latest research progress is valuable for promoting the progress in this field.

This Special Issue will pay special attention to algorithms and computational complexity. Potential topics to be submitted include, but are not limited to, the following areas:

  • Model solving algorithms
  • AI algorithms
  • Simulation algorithms
  • Data fitting algorithms
  • Intelligent optimization algorithms
  • Spatial complexity of algorithms
  • Time complexity of algorithms
  • Uncertain computability

Prof. Dr. Yi Zhao
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 submissions that pass pre-check are 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. Applied Sciences is an international peer-reviewed open access semimonthly 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 2400 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

  • algorithms
  • computational complexity

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Published Papers (1 paper)

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Research

17 pages, 981 KiB  
Article
VLSI-Friendly Filtering Algorithms for Deep Neural Networks
by Aleksandr Cariow, Janusz P. Papliński and Marta Makowska
Appl. Sci. 2023, 13(15), 9004; https://doi.org/10.3390/app13159004 - 6 Aug 2023
Cited by 1 | Viewed by 819
Abstract
The paper introduces a range of efficient algorithmic solutions for implementing the fundamental filtering operation in convolutional layers of convolutional neural networks on fully parallel hardware. Specifically, these operations involve computing M inner products between neighbouring vectors generated by a sliding time window [...] Read more.
The paper introduces a range of efficient algorithmic solutions for implementing the fundamental filtering operation in convolutional layers of convolutional neural networks on fully parallel hardware. Specifically, these operations involve computing M inner products between neighbouring vectors generated by a sliding time window from the input data stream and an M-tap finite impulse response filter. By leveraging the factorisation of the Hankel matrix, we have successfully reduced the multiplicative complexity of the matrix-vector product calculation. This approach has been applied to develop fully parallel and resource-efficient algorithms for M values of 3, 5, 7, and 9. The fully parallel hardware implementation of our proposed algorithms achieves approximately a 30% reduction in embedded multipliers compared to the naive calculation methods. Full article
(This article belongs to the Special Issue Recent Developments in Algorithms and Computational Complexity)
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