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Electronics
Special Issues
High-Performance Computing and AI Compression
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High-Performance Computing and AI Compression

A special issue of Electronics (ISSN 2079-9292). This special issue belongs to the section "Computer Science & Engineering".

Deadline for manuscript submissions: 15 July 2025 | Viewed by 1215

Special Issue Editors

Dr. Siye Wang

E-Mail Website
Guest Editor
School of Artificial Intelligence, Beijing University of Posts and Telecommunications, Beijing 100876, China
Interests: high-performance computing and artificial intelligence; high-performance digital signal processing
Dr. Man Zhang

E-Mail Website
Guest Editor
School of Artificial Intelligence, Beijing University of Posts and Telecommunications, Beijing 100876, China
Interests: computer vision; pattern recognition

Special Issue Information

Dear Colleagues,

The ever-growing demand for processing power and data storage in today's digital landscape has brought high-performance computing (HPC) and artificial intelligence (AI) to the forefront of technological advancement. As these fields continue to evolve, the challenge of efficiently managing and compressing vast amounts of data without compromising on speed or accuracy has become increasingly critical.

This Special Issue on "High-Performance Computing and AI Compression" in Electronics aims to explore the latest innovations and developments in this area, addressing both theoretical and practical aspects. We invite original research articles, reviews, and case studies that contribute to the understanding and advancement of compression techniques tailored for HPC and AI applications.

Research areas of this Special Issue may include (but are not limited to) the following:

  • Novel algorithms for data compression in HPC and AI environments;
  • Hardware acceleration techniques for efficient data processing and compression;
  • Case studies demonstrating the practical implementation of compression techniques in real-world HPC and AI applications;
  • Theoretical advancements in lossless and lossy compression methods;
  • Interdisciplinary approaches combining AI with traditional compression techniques;
  • Benchmarking and performance evaluation of compression methods in HPC and AI scenarios;
  • Energy-efficient compression strategies for sustainable high-performance computing.

We encourage contributions from researchers and practitioners across academia and industry to share their latest findings and insights, fostering a comprehensive discussion on the challenges and opportunities in this vital area of research.

We look forward to your valuable contributions to this Special Issue.

Dr. Siye Wang
Dr. Man Zhang
Guest Editors

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. Electronics 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

  • high-performance computing (HPC)
  • AI compression
  • data compression
  • lossless compression
  • hardware acceleration
  • energy efficiency
  • algorithm optimization
  • real-time processing

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Published Papers (1 paper)

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Research

15 pages, 1363 KiB  
Article
MSQuant: Efficient Post-Training Quantization for Object Detection via Migration Scale Search
by Zhesheng Jiang, Chao Li, Tao Qu, Chu He and Dingwen Wang
Electronics 2025, 14(3), 504; https://doi.org/10.3390/electronics14030504 - 26 Jan 2025
Viewed by 954
Abstract
YOLO (You Only Look Once) has become the dominant paradigm in real-time object detection. However, deploying real-time object detectors on resource-constrained platforms faces challenges due to high computational and memory demands. Quantization addresses this by compressing and accelerating CNN models through the representation [...] Read more.
YOLO (You Only Look Once) has become the dominant paradigm in real-time object detection. However, deploying real-time object detectors on resource-constrained platforms faces challenges due to high computational and memory demands. Quantization addresses this by compressing and accelerating CNN models through the representation of weights and activations with low-precision values. Nevertheless, the quantization difficulty between weights and activations is often imbalanced. In this work, we propose MSQuant, an efficient post-training quantization (PTQ) method for CNN-based object detectors, which balances the quantization difficulty between activations and weights through migration scale. MSQuant introduces the concept of migration scales to mitigate this disparity, thereby improving overall model accuracy. An alternating search method is employed to optimize the migration scales, avoiding local optima and reducing quantization error. We select YOLOv5 and YOLOv8 models as the PTQ baseline, followed by extensive experiments on the PASCAL VOC, COCO, and DOTA datasets to explore various combinations of quantization methods. The results demonstrate the effectiveness and robustness of MSQuant. Our approach consistently outperforms other methods, showing significant improvements in quantization performance and model accuracy. Full article
(This article belongs to the Special Issue High-Performance Computing and AI Compression)
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