Design and Implementation for Compressor

Abstract

Approximate multipliers attract a large interest in the scientific literature that proposes several circuits built with approximate 4-2 compressors. Due to the large number of proposed solutions, the designer who wishes to use an approximate 4-2 compressor is faced with the problem of selecting the right topology. In this project, we present a comprehensive survey and comparison of approximate 4-2 compressors previously proposed in literature. We present also a novel approximate compressor, so that a total of twelve different approximate 4-2 compressors are analyzed. The investigated circuits are employed to design 8 × 8 and 16× 16 multipliers, implemented in CMOS technology. For each operand size we analyze two multiplier configurations, with different levels of approximations, both signed and unsigned. Our study highlights that there is no unique winning approximate compressor topology since the best solution depends on the required precision, on the signedness of the multiplier and on the considered error metric.

Introduction

Approximate computing is a design approach that reduces power consumption and improves performance by allowing controlled computational errors, suitable for error-resilient applications like multimedia, machine learning, and DSP systems. Among key components, approximate multipliers—and specifically 4:2 compressors within them—are critical for energy-efficient arithmetic operations.

The 4:2 compressor reduces partial product bits in multipliers, enabling faster and lower-power designs. Approximate versions simplify logic, lowering transistor count and power use while maintaining acceptable accuracy. This research focuses on designing and analyzing low-power, approximate 4:2 compressors, balancing energy efficiency, speed, and error tolerance.

The methodology involves circuit-level optimizations (logic simplification, transistor sizing, alternative logic styles), verification through exhaustive simulations, and ASIC synthesis using tools like Cadence Genus. The design is validated with functional verification and power-delay-area benchmarking, demonstrating significant power savings with minimal impact on performance or accuracy.

Implementations on modern technologies such as FinFET and FD-SOI further enhance power efficiency, making these compressors ideal for battery-powered and energy-constrained devices. The work aims to provide optimized compressor architectures for low-power multipliers applicable in AI accelerators, DSP, and mobile computing, contributing to energy-conscious digital system designs.

Conclusion

The thesis presented in this thesis successfully develops and analyses low-power 4-2 compressor architectures tailored for energy-efficient multipliers in error-resilient applications. By exploring various innovative circuit-level optimizations and approximation techniques, the study achieves a significant reduction in both dynamic and static power consumption while maintaining acceptable levels of computational accuracy. The introduction of standardized evaluation metrics allows for a more comprehensive comparison among different compressor designs, providing a clearer framework for assessing trade-offs between power efficiency and performance reliability. This work contributes crucial insights into the development of sustainable and high-performance digital systems, particularly in domains like AI accelerators and digital signal processing. Furthermore, the findings indicate that employing approximate compressors not only enhances energy savings but also enables the design of multipliers that can tolerate minor inaccuracies, aligning with the growing demand for energy-efficient computing solutions. The systematic methodology applied throughout the research ensures that the proposed low- power compressors can be effectively integrated across various applications, paving the way for future advancements in arithmetic circuit designs. As the need for power-efficient solutions continues to rise, this research lays a solid foundation for ongoing exploration and innovation in the field, with practical implications for next-generation embedded and portable devices.

References

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Copyright

Copyright © 2025 Sai Irikedandla , Madhuri Vemuluri . 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.