Rounding Technique Analysis for Power-Area & Energy Efficient Approximate Multiplier Design

• Published in: 2019 IEEE 9th Annual Computing and Communication Workshop and Conference (CCWC) pp. 0420 - 0425
• Main Authors: Lohray, Priyanka, Gali, Satwik, Rangisetti, Srirathan, Nikoubin, Tooraj
• Format: Conference Proceeding
• Language: English
• Published: IEEE 01.01.2019
• Subjects: Adders; Approximate computing; Compressors; Data Processing; Digital Arithmetic; Energy efficiency; Energy efficient; Hardware; Hi-performance; Image coding; Logic gates; Rounding Technique
• DOI: 10.1109/CCWC.2019.8666472

Approximate computing is one of best suited efficient data processing for error resilient applications, such as signal and image processing, computer vision, machine learning, data mining etc. Approximate computing reduces accuracy which is acceptable as a cost of increasing the circuit characteristics depends on the application. Desirable accuracy is the threshold point for controlling the trade off, between accuracy and circuit characteristics under the control of the circuit designer. In this work, the rounding technique is introduced as an efficient method for controlling this trade off. In this regard multiplier circuits as a critical building block for computing in most of the processors have been considered for the evaluation of the rounding technique efficiency. The impact of the rounding method is investigated by comparison of circuit characteristics for three multipliers. These three multipliers are the conventional Wallace tree accurate multiplier, DRUM [4] the recently proposed approximate multiplier and the rounded based approximate multiplier proposed in this work. Simulation results for three selected technologies show significant improvement on the circuit characteristics in terms of power, area, speed, and energy for proposed multiplier in comparison with their counterparts. Input data rounding pattern and the probability of the repetition for rounded values has been introduced as two essential items to control the level of the accuracy for each range of the data with minimum cost on the hardware.