Approximate Recursive Multipliers Using Low Power Building Blocks

• Published in: IEEE transactions on emerging topics in computing Vol. 10; no. 3; pp. 1315 - 1330
• Main Authors: Zacharelos, Efstratios, Nunziata, Italo, Saggese, Gerardo, Strollo, Antonio G.M., Napoli, Ettore
• Format: Journal Article
• Language: English
• Published: New York IEEE 01-07-2022; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Adders; Approximate methods; Arithmetic; arithmetic and logic structures; Artificial neural networks; Circuit design; Compressors; Convolutional neural networks; Energy dissipation; error handling and recovery; Error reduction; FinFETs; Information technology; integrated circuits; Logic gates; Multipliers; Power consumption
• ISSN: 2168-6750; 2168-6750
• DOI: 10.1109/TETC.2022.3186240

Approximate computing, frequently used in error tolerant applications, aims to achieve higher circuit performances by allowing the possibility of inaccurate results, rather than guaranteeing a correct outcome. Many contributions target the binary multiplier aiming to minimize the complexity of this common yet power-hungry circuit. Approximate recursive multipliers are low-power designs that exploit approximate building blocks to scale up to their final size. In this paper, we present two novel 4×4 approximate multipliers obtained by carry manipulation. They are used to compose 8×8 designs with different error-performance trade-off. The final circuits exhibit a competitive behavior in terms of error while reducing the power dissipation when compared to state-of-the-art proposals. The proposed multipliers and state-of-the-art designs found in the literature, have been synthesized targeting a 14nm FinFET technology to determine the electrical characteristics. Compared with an exact 8×8 multiplier, the least dissipative design proposed in this paper reduces power consumption and silicon area by 46%, and minimum delay by 21%. It also consumes 14% less power than the least power-hungry recursive circuit found in the literature, while offering 81% higher accuracy. Ιmage processing applications and a convolutional neural network are shown to demonstrate the effectiveness of the proposed multipliers.