Scaled Population Subtraction for Approximate Computing
Scaled Population Subtraction for Approximate Computing
复制标题
DOI：
10.1109/iccd50377.2020.00065
复制
发表时间：
2020-10
期刊：
影响因子：
--
通讯作者：
K. Bharathi;Jiang Hu;S. Khatri
中科院分区：
文献类型：
--
作者：
K. Bharathi;Jiang Hu;S. Khatri
本文提出了比例总体减法算法，以填补比例总体算法中的一个空白。比例种群算法（Scaled population arithmetic，SP）是一种受随机计算（stochastic computing，SC）启发而提出的算法，SC是一种非传统的近似计算方法，以其简单性、面积效率和对比特错误的恢复能力而闻名。SP算法与SC相比减少了数值误差，并且还解决了SC的串行化限制，因为它被设计为具有O（1）门延迟。以前，SP仅限于加法和乘法，没有执行减法的方法。本文首先介绍了SP减法的基本思想，然后详细研究了几种改进基本设计以减小计算误差的方法。与我们的基本SP减法思想相比，我们最好的SP设计显着改善了错误（减少了32.3%）。我们还研究了平衡的设计复杂性的SP的输出误差。此外，我们的实现的SP？具有改善的延迟，功率和面积相比，固定点实现相同的大小。
In this paper we present Scaled Population Subtraction to fill a void in Scaled Population arithmetic. Scaled population (SP) arithmetic is a scheme that is inspired by stochastic computing (SC), a non-conventional approximate computing method that is well known for its simplicity, area efficiency and resilience to bit errors. SP arithmetic reduces the numerical errors compared to SC and also solves the serialization limitation of SC, since it is designed to have a O(1) gate delay. Previously, SP was limited to only addition and multiplication and did not have a way to perform subtraction. This paper introduces the basic SP subtraction idea, followed by a detailed study of several ways that the basic design can be improved to reduce the computational error. Our best SP design significantly improves the error compared to our basic SP subtraction idea (reducing it by 32.3%). We also study the trade-off between design complexity of the SP subtractor against output error. Also, our implementation of the SP subtractor exhibits an improved delay, power and area compared to fixed point realizations with the same size.