An Energy-Efficient Approximate Divider Based on Logarithmic Conversion and Piecewise Constant Approximation

对数 分段 常量(计算机编程) 数学 近似误差 能量(信号处理) 离散数学 除数(代数几何) 整数(计算机科学) 算法 计算机科学 应用数学 数学分析 统计 程序设计语言
作者
Yong Wu,Honglan Jiang,Zining Ma,Pengfei Gou,Yong Lu,Jie Han,Shouyi Yin,Shaojun Wei,Leibo Liu
出处
期刊:IEEE Transactions on Circuits and Systems I-regular Papers [Institute of Electrical and Electronics Engineers]
卷期号:69 (7): 2655-2668 被引量:8
标识
DOI:10.1109/tcsi.2022.3167894
摘要

Approximate computing (AC) has been considered as a promising paradigm to improve the energy-efficiency of computing hardware for error-tolerant applications, with negligible quality degradation to the output. Dividers frequently limit the performance of a computing system; however, they have not received as much attention as multipliers and adders in AC. In this paper, an energy-efficient and high-performance approximate divider is proposed based on logarithmic conversion and piecewise constant approximation. In this design, the range for the conversion between binary and logarithmic numbers is first expanded from $\mathbf {[{0,1}]}$ to $\mathbf {[-0.5,1]}$ . A heuristic search algorithm is then devised to find the most accurate constant set to approximate the reciprocal of the divisor, by minimizing a statistical error. The hardware implementation is presented for both floating-point (FP) and integer dividers. With a high configurability, the proposed divider results in a mean relative error distance (MRED) from 2.78% to 0.046%, indicating a high accuracy among state-of-the-art approximate dividers. Compared to the half-precision FP divider, the proposed divider with a MRED of 0.74% can achieve nearly $\mathbf {90\times }$ improvement in PDP. Moreover, compared to state-of-the-art approximate dividers, the proposed design is in the Pareto Frontier in terms of power delay product (PDP) and MRED. The three image processing application results demonstrate that the proposed divider can result in the highest peak signal-to-noise ratio (PSNR) and structural similarity (SSIM) even with truncation.

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