HPR-Mul: An Area and Energy-Efficient High-Precision Redundancy Multiplier by Approximate Computing
Jafar Vafaei, Omid Akbari
Abstract
For critical applications that require a higher level of reliability, the triple modular redundancy (TMR) scheme is usually employed to implement fault-tolerant arithmetic units. However, this method imposes a significant area and power/energy overhead. Also, the majority-based voter in the typical TMR designs is highly sensitive to soft errors and the design diversity of the triplicated module, which may result in an error for a small difference between the output of the TMR modules. However, a wide range of applications deployed in critical systems are inherently error-resilient, that is, they can tolerate some inexact results at their output while having a given level of reliability. In this article, we propose a high precision redundancy multiplier (HPR-Mul) that relies on the principles of approximate computing to achieve higher energy efficiency and lower area, as well as resolve the aforementioned challenges of the typical TMR schemes, while retaining the required level of reliability. The HPR-Mul is composed of full precision (FP) and two reduced precision (RP) multipliers, along with a simple voter to determine the output. Unlike the state-of-the-art RP redundancy multipliers (RPR-Muls) that require a complex voter, the voter of the proposed HPR-Mul is designed based on mathematical formulas resulting in a simpler structure. Furthermore, we use the intermediate signals of the FP multiplier as the inputs of the RP multipliers, which significantly enhance the accuracy of the HPR-Mul. The efficiency of the proposed HPR-Mul is evaluated in a 15-nm FinFET technology, where the results show up to 70% and 69% lower power consumption and area, respectively, compared to the typical TMR-based multipliers. Also, the HPR-Mul outperforms the state-of-the-art RPR-Mul by achieving up to 84% higher soft error tolerance. Moreover, by employing the HPR-Mul in different image processing applications, up to 13% higher output image quality is achieved in comparison with the state-of-the-art RPR multipliers.