Advancements in FPGA?Based Design of Fixedpoint FIR Filters for Multirate Signal Processing Applications: A Comprehensive Review

Abstract

Field?programmable gate arrays (FPGAs) have emerged as a pivotal technology in the implementation of digital signal processing algorithms, particularly for fixed-point finite impulse response (FIR) filters in multirate applications. Recent advancements have significantly improved performance, power efficiency, and adaptability in harsh real?time environments. This review synthesizes thirty studies that span innovations in design methodologies, architecture optimization, and practical deployments. It explores fixed-point arithmetic benefits and challenges in FIR filter implementations, discusses multirate signal processing techniques, and identifies emerging trends such as adaptive filter structures and low? latency architectures. Emphasis is placed on FPGA-specific optimizations, including resource utilization and parallel processing strategies. The review concludes by highlighting potential research directions and the need for further integration of artificial intelligence tools to enhance design automation. These insights will benefit researchers and engineers seeking robust, high-performance FPGA designs in complex signal processing environments.

Introduction

The rapid advancement of digital signal processing (DSP) over the last decade has focused on enhancing performance and reducing power consumption, with finite impulse response (FIR) filters playing a key role, especially in multirate signal processing involving sampling rate conversion. Field-programmable gate arrays (FPGAs) have enabled efficient, fixed-point FIR filter implementations that leverage parallelism for high throughput and resource efficiency.

This review surveys 30 studies on FPGA-based fixed-point FIR filters for multirate processing, covering innovations in algorithms, architectures, and synthesis techniques. Fixed-point arithmetic, preferred for its lower complexity compared to floating-point, presents challenges like word-length optimization, quantization noise, and resource constraints, which become more complex in multirate environments.

The surveyed works showcase diverse approaches such as pipeline architectures, polyphase decomposition, multiplierless designs, dynamic reconfiguration, and adaptive filtering, all aimed at improving speed, accuracy, power efficiency, and resource utilization. Key techniques include exploiting FPGA parallelism, pipelining, distributed arithmetic, and scheduling algorithms for dynamic coefficient adjustment.

Conclusion

In summary, advancements in FPGA-based design of fixed-point FIR filters have substantially impacted multi rate signal processing applications. The studies reviewed herein provide valuable insights into various optimization techniques, from pipeline architectures and distributed arithmetic to dynamic reconfiguration and adaptive filtering. These innovations have collectively led to reductions in latency, improved resource utilization, and enhanced overall system performance. While challenges such as quantization noise and interconnect delays persist, ongoing research promises further breakthroughs. Future efforts should concentrate on integrating machine learning for automated design optimizations, refining adaptive architectures, and exploring hybrid computational models that combine fixed-point and floating-point arithmetic. Such end eavors will be crucial in meeting the ever-growing demands for efficient, high performance digital signal processing systems in modern communication, multimedia, and control applications. The reviewed literature not only highlights the state-of-the-art but also charts a path forward for future innovations in this rapidly evolving field.

References

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Copyright © 2025 Swati Soni, Nisha Chauhan, Nidhi Sharma . This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.