Towards High-Performance Network Coding: FPGA Acceleration With Bounded-value Generators
🤖 AI Summary
To address the computational intensity, high power consumption, and hardware inefficiency hindering practical deployment of BATS network coding, this paper proposes a co-optimization framework integrating algorithmic innovation and hardware design. We introduce CS-BATS, a hardware-friendly variant featuring a bounded-value generator that reduces finite-field multiplier area by up to 70%. Coupled with an FPGA-accelerated architecture, optimized finite-field arithmetic, and customized batched sparse code structures, the implementation achieves a throughput of 27 Gbps—over 300× faster than pure software implementations. The design exhibits high scalability and resource efficiency, enabling energy-efficient, high-performance hardware acceleration of BATS coding. This work establishes a new paradigm for deploying BATS codes in resource-constrained, high-throughput networking scenarios.
Application Category
📝 Abstract
Network coding enhances performance in network communications and distributed storage by increasing throughput and robustness while reducing latency. Batched Sparse (BATS) codes are a class of capacity-achieving network codes, but their practical applications are hindered by their structure, computational intensity, and power demands of finite field operations. Most literature focuses on algorithmic-level techniques to improve coding efficiency. Optimization with an algorithm/hardware co-designing approach has long been neglected. Leveraging the unique structure of BATS codes, we first present CS-BATS, a hardware-friendly variant. Next we propose a simple but effective bounded-value generator, to reduce the size of a finite field multiplier by up to 70%. Finally, we report on a scalable and resource-efficient FPGA-based network coding accelerator that achieves a throughput of 27 Gbps, a speedup of more than 300 over software.
Problem
Research questions and friction points this paper is trying to address.
Network Coding Performance
Computational Complexity
Energy Consumption
Innovation
Methods, ideas, or system contributions that make the work stand out.
Hardware-friendly BATS Coding
FPGA Accelerator
Energy-efficient Computing
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