Hardware/Algorithm Co-Design for Communication and Networking Acceleration

A special issue of Electronics (ISSN 2079-9292). This special issue belongs to the section "Networks".

Deadline for manuscript submissions: 15 August 2025 | Viewed by 289

Special Issue Editor


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Guest Editor
School of Microelectronics, Xi’an Jiaotong University, Xi’an 710049, China
Interests: neural network accelerator; reconfigurable computing; accelerator for communication and networking; VLSI SoC design
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Special Issue Information

Dear Colleagues,

Electronics invites manuscript submissions in the area of hardware/algorithm co-design for communication and networking acceleration. In the rapidly evolving landscape of communication and networking, the demands placed on both hardware and software have escalated significantly. The complexity of communication scenarios has grown with the advent of 5G and beyond, where traditional algorithms struggle to cope with high-speed, low-latency, and high-reliability requirements. Meanwhile, network transmission speeds have surged to 100GbE and beyond, accompanied by an explosion in the number of connected devices, leading to increasingly intricate network topologies. This transformation necessitates new paradigms such as Software-Defined Networking (SDN) and Network Function Virtualization (NFV), which introduce additional layers of abstraction and flexibility but also demand more sophisticated management and processing capabilities. Given these challenges, it is evident that conventional algorithmic approaches are no longer sufficient to meet the performance and efficiency needs of modern communication and networking systems. Leveraging hardware-based or hardware–software co-design solutions can provide a significant advantage. By utilizing platforms like Application-Specific Integrated Circuits (ASICs) or Field-Programmable Gate Arrays (FPGAs), researchers and engineers can develop tailored accelerators that not only enhance system performance but also reduce power consumption and cost. Moreover, the integration of artificial intelligence (AI) into communication and networking has shown promise in optimizing resource allocation, improving security, and enabling adaptive, self-managing networks.

This Special Issue of Electronics aims to bring together cutting-edge research in hardware/algorithm co-design for communication and networking acceleration. It seeks to explore innovative methodologies, architectures, and technologies that address the current limitations and open up new possibilities for future systems. By fostering a deeper understanding of how hardware and software can be synergistically combined, this issue will contribute to the development of more efficient, scalable, and intelligent communication and networking infrastructures.

We welcome original research articles and reviews on topics including but not limited to the following:

1. Hardware/Software Co-Design for Diverse Communication Scenarios:
      • Acceleration strategies for large-scale MIMO, millimeter-wave, terahertz communications, and efficient signal processing on hardware platforms, focusing on low-power and high-throughput designs.
2. Co-Design Solutions for Advanced Networking Paradigms:
      • Hardware-accelerated packet classification, congestion control, resource management, and virtualization techniques for SDN, NFV, and emerging network architectures to enhance performance and scalability.
3. Hardware Offloading for Network Modules:
      • Techniques for accelerating network protocol stacks, encryption, decryption, and compute-intensive tasks in edge and cloud environments, aimed at reducing latency and improving throughput in data centers.
4. AI-Driven Co-Design for Communication and Networking:
      • Integration of machine learning and AI for predictive maintenance, anomaly detection, network management, automation, and dynamic resource allocation, leveraging reinforcement learning for adaptive systems.

5. Emerging Architectures and Acceleration Techniques:

      • Novel architectures and advanced methods for next-generation communication systems.

Dr. Chen Yang
Guest Editor

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Keywords

  • hardware/algorithm co-design
  • communication systems
  • networking
  • ASIC
  • FPGA
  • large-scale MIMO
  • SDN
  • NFV
  • packet classification
  • congestion control
  • AI
  • machine learning
  • network acceleration
  • hardware offloading
  • emerging architectures

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Published Papers (1 paper)

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Research

22 pages, 882 KiB  
Article
HLSCAM: Fine-Tuned HLS-Based Content Addressable Memory Implementation for Packet Processing on FPGA
by Mostafa Abbasmollaei, Tarek Ould-Bachir and Yvon Savaria
Electronics 2025, 14(9), 1765; https://doi.org/10.3390/electronics14091765 - 26 Apr 2025
Viewed by 164
Abstract
Content Addressable Memories (CAMs) are pivotal in high-speed packet processing systems, enabling rapid data lookup operations essential for applications such as routing, switching, and network security. While traditional Register-Transfer Level (RTL) methodologies have been extensively used to implement CAM architectures on Field-Programmable Gate [...] Read more.
Content Addressable Memories (CAMs) are pivotal in high-speed packet processing systems, enabling rapid data lookup operations essential for applications such as routing, switching, and network security. While traditional Register-Transfer Level (RTL) methodologies have been extensively used to implement CAM architectures on Field-Programmable Gate Arrays (FPGAs), they often involve complex, time-consuming design processes with limited flexibility. In this paper, we propose a novel templated High-Level Synthesis (HLS)-based approach for the design and implementation of CAM architectures such as Binary CAMs (BCAMs) and Ternary CAMs (TCAMs) optimized for data plane packet processing. Our HLS-based methodology leverages the parallel processing capabilities of FPGAs through employing various design parameters and optimization directives while significantly reducing development time and enhancing design portability. This paper also presents architectural design and optimization strategies to offer a fine-tuned CAM solution for networking-related arbitrary use cases. Experimental results demonstrate that HLSCAM achieves a high throughput, reaching up to 31.18 Gbps, 9.04 Gbps, and 33.04 Gbps in the 256×128, 512×36, and 1024×150 CAM sizes, making it a competitive solution for high-speed packet processing on FPGAs. Full article
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