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Micromachines
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Recent Advances in Field-Programmable Gate Array (FPGA)
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Recent Advances in Field-Programmable Gate Array (FPGA)

A special issue of Micromachines (ISSN 2072-666X). This special issue belongs to the section "E：Engineering and Technology".

Deadline for manuscript submissions: 28 February 2026 | Viewed by 1743

Special Issue Editors

Prof. Dr. He Li

E-Mail Website
Guest Editor
School of Electronic Science and Engineering, Southeast University, Nanjing 210096, China
Interests: field-programmable gate array (FPGA); quantum computing; AI acceleration
Dr. Tarek Ould-Bachir

E-Mail Website
Guest Editor
MOTCE Laboratory, Department of Computer Engineering, Polytechnique Montréal, Montréal, QC H3T 1J4, Canada
Interests: field-programmable gate arrays (FPGAs); computer architecture; embedded systems
Dr. Philippos Papaphilippou

E-Mail Website
Guest Editor
Electrical and Electronic Engineering, University of Southampton, Southampton, UK
Interests: field-programmable gate arrays (FPGAs); system interconnects and network-on-chips (NoC); big data analytics and sorting accelerators; many-core computer architecture

Special Issue Information

Dear Colleagues,

Field-programmable gate arrays (FPGAs) have successfully transitioned from mostly prototyping platforms to heterogeneous compute-acceleration platforms. Currently, FPGAs have been widely used in artificial intelligence (AI) acceleration, sensor signal acquisition and processing, as well as quantum information processing. With the rapid advances in AI, quantum computing, and micro-nano sensor systems, FPGAs continue to be an attractive computing platform for domain-specific accelerations. In this Special Issue, we call for high-quality and insightful manuscripts on advanced FPGA circuits and systems designs, modern FPGA architectures, high-level synthesis tools and FPGA-based applications, such as AI or LLM acceleration, quantum computing and quantum information, scientific computing, integration of sensor arrays, etc. The objective of this Special Issue is to solicit and present the latest research findings in the field of hardware and algorithm codesign with a particular interest in FPGA circuits and systems.

We look forward to receiving your submissions!

Prof. Dr. He Li
Dr. Tarek Ould-Bachir
Dr. Philippos Papaphilippou
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 250 words) can be sent to the Editorial Office for assessment.

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. Micromachines 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 2100 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

  • FPGA architecture
  • FPGA circuits and systems design
  • FPGA-based signal processing with micro- and nanodevices
  • emerging applications: AI or LLM acceleration, neuromorphic emulation, quantum computing and quantum information, scientific computing, etc.

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Published Papers (1 paper)

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Research

23 pages, 3153 KB  
Article
Domain-Specific Acceleration of Gravity Forward Modeling via Hardware–Software Co-Design
by Yong Yang, Daying Sun, Zhiyuan Ma and Wenhua Gu
Micromachines 2025, 16(11), 1215; https://doi.org/10.3390/mi16111215 - 25 Oct 2025
Viewed by 908
Abstract
The gravity forward modeling algorithm is a compute-intensive method and is widely used in scientific computing, particularly in geophysics, to predict the impact of subsurface structures on surface gravity fields. Traditional implementations rely on CPUs, where performance gains are mainly achieved through algorithmic [...] Read more.
The gravity forward modeling algorithm is a compute-intensive method and is widely used in scientific computing, particularly in geophysics, to predict the impact of subsurface structures on surface gravity fields. Traditional implementations rely on CPUs, where performance gains are mainly achieved through algorithmic optimization. With the rise of domain-specific architectures, FPGA offers a promising platform for acceleration, but faces challenges such as limited programmability and the high cost of nonlinear function implementation. This work proposes an FPGA-based co-processor to accelerate gravity forward modeling. A RISC-V core is integrated with a custom instruction set targeting key computation steps. Tasks are dynamically scheduled and executed on eight fully pipeline processing units, achieving high parallelism while retaining programmability. To address nonlinear operations, we introduce a piecewise linear approximation method optimized via stochastic gradient descent (SGD), significantly reducing resource usage and latency. The design is implemented on the AMD UltraScale+ ZCU102 FPGA (Advanced Micro Devices, Inc. (AMD), Santa Clara, CA, USA) and evaluated across several forward modeling scenarios. At 250 MHz, the system achieves up to 179× speedup over an Intel Xeon 5218R CPU (Intel Corporation, Santa Clara, CA, USA) and improves energy efficiency by 2040×. To the best of our knowledge, this is the first FPGA-based gravity forward modeling accelerate design. Full article
(This article belongs to the Special Issue Recent Advances in Field-Programmable Gate Array (FPGA))
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