New Trends in FPGAs-Based Accelerators for Deep Neural Networks
A special issue of Electronics (ISSN 2079-9292). This special issue belongs to the section "Computer Science & Engineering".
Deadline for manuscript submissions: 15 November 2025 | Viewed by 20
Special Issue Editors
Interests: design of approximate arithmetic units; approximate caches; hardware accelerators for deep neural networks
Interests: embedded systems; machine learning; approximate computing; reconfigurable computing; reliability-aware computing systems; system-level design
Special Issue Information
Dear Colleagues,
The aim of this Special Issue is to bring together the latest research and developments in the field of FPGA-based accelerators for deep neural networks. It provides a platform for researchers, engineers, and practitioners to share their insights, innovative ideas, and practical experiences. In doing so, we aim to advance FPGA-based acceleration techniques for deep neural networks, ultimately enhancing the performance, efficiency, and scalability of these crucial computational systems in various applications.
The scope of this issue encompasses a wide range of topics related to the utilization of FPGAs in accelerating deep neural networks. It covers theoretical studies exploring novel algorithms, architectures tailored toward FPGA implementation, and practical engineering efforts in developing and optimizing real-world FPGA-based accelerator systems.
This Special Issue will focus on (but is not limited to) the following topics:
- Novel FPGA architecture specifically designed for different types of deep neural networks, including convolutional neural networks (CNNs), recurrent neural networks (RNNs), and their variants.
- Optimization techniques for mapping deep neural network models onto FPGAs, including resource allocation, scheduling, and dataflow management.
- Hardware–software co-design methodologies to achieve the seamless integration and efficient operation of FPGA accelerators with host systems running deep neural network applications.
- Case studies and practical applications that demonstrate the effectiveness of FPGA-based accelerators in areas like computer vision, natural language processing, and autonomous systems.
- Energy-efficient design strategies for FPGA-based deep neural network accelerators to meet the demands of power-constrained environments.
Dr. Salim Ullah
Dr. Siva Satyendra Sahoo
Guest Editors
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Keywords
- FPGA
- deep neural networks
- accelerator architecture
- optimization techniques
- energy efficiency
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