Manuscript Number : IJSRST2310116

Implementation of Novel Approximate Adder Using Parallel Prefix Structure

Authors(2) :-G Indra Kumar, Dr. B. Sudharani

This article offers a unique approximation adder which uses error-reduced carry prediction and constant reduction in conjunction with error reduction techniques. The suggested adder design actually improve computing accuracy while also improving hardware effectiveness. In comparison to the approximate adders studied in this research, the proposed carry forecast technique can elevate prediction error rates. The error reduction technique also enhances overall computation efficiency by lowering the error distance (ED). The suggested adder is one of the most economical because it has the effective design compromise of the adders beneath analysis. We also show that whenever the postulated adder is used in application areas such as digital image processing and machine learning, the approximation errors caused by the adder have even less influence on quality attributes.

Authors and Affiliations

G Indra Kumar
M.Tech, Department of Electronics and Communication Engineering, Sri Venkateswara College of Engineering, Tirupati, India.
Dr. B. Sudharani
Associate Professor, Department of Electronics and Communication Engineering, Sri Venkateswara College of Engineering, Tirupati, India.

Error Distance, Approximate Adder.

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Publication Details

Published in : Volume 10 | Issue 1 | January-February 2023
Date of Publication : 2023-02-28
License:  This work is licensed under a Creative Commons Attribution 4.0 International License.
Page(s) : 151-158
Manuscript Number : IJSRST2310116
Publisher : Technoscience Academy

Print ISSN : 2395-6011, Online ISSN : 2395-602X

Cite This Article :

G Indra Kumar, Dr. B. Sudharani, " Implementation of Novel Approximate Adder Using Parallel Prefix Structure", International Journal of Scientific Research in Science and Technology(IJSRST), Print ISSN : 2395-6011, Online ISSN : 2395-602X, Volume 10, Issue 1, pp.151-158, January-February-2023.
Journal URL : https://ijsrst.com/IJSRST2310116
Citation Detection and Elimination     |      | | Y. Kim, Y. Zhang, and P. Li,   A reconfigurable digital neuromorphic processor with memristive synaptic crossbar for cognitive computing,   ACM J. Emerg. Technol. Comput. Syst., vol. 11, no. 4, pp. 38:1–38:25, Apr. 2015.
  • B. Liu, Z. Wang, W. Zhu, Y. Sun, Z. Shen, L. Huang, Y. Li, Y. Gong, and W. Ge,   An ultra-low power always-on keyword spotting accelerator using quantized convolutional neural network and voltage-domain analog switching network-based approximate computing,   IEEE Access, vol. 7, pp. 186456–186469, 2019.
  • I. Khan, S. Choi, and Y.-W. Kwon,   Earthquake detection in a static and dynamic environment using supervised machine learning and a novel feature extraction method,   Sensors, vol. 20, no. 3, p. 800, Feb. 2020.
  • Q. Wang, P. Li, and Y. Kim, A parallel digital VLSI architecture for integrated support vector machine training and classification, IEEE Trans. Very Large Scale Integr. (VLSI) Syst., vol. 23, no. 8, pp. 1471–1484, Aug. 2015.
  • V. Gupta, D. Mohapatra, A. Raghunathan, and K. Roy,   Low-power digital signal processing using approximate adders,   IEEE Trans. Comput.-Aided Design Integr. Circuits Syst., vol. 32, no. 1, pp. 124–137, Jan. 2013.
  • Y. S. Yang and Y. Kim,   Approximate digital leaky Integrate-and-fire neurons for energy efficient spiking neural networks,   IEIE Trans. Smart Process. Comput., vol. 9, no. 3, pp. 252–259, Jun. 2020.
  • A. Raha, H. Jayakumar, and V. Raghunathan,   Input-based dynamic reconfiguration of approximate arithmetic units for video encoding,   IEEE Trans. Very Large Scale Integr. (VLSI) Syst., vol. 24, no. 3, pp. 846–857, Mar. 2016.
  • T. Moreau, A. Sampson, and L. Ceze,   Approximate computing: Making mobile systems more efficient,   IEEE Pervasive Comput., vol. 14, no. 2, pp. 9–13, Apr. 2015.
  • S. Mittal,   A survey of techniques for approximate computing,   ACM Comput. Surv., vol. 48, no. 4, pp. 1–33, May 2016.
    • " target="_blank"> BibTeX     | Y. Kim, Y. Zhang, and P. Li,   A reconfigurable digital neuromorphic processor with memristive synaptic crossbar for cognitive computing,   ACM J. Emerg. Technol. Comput. Syst., vol. 11, no. 4, pp. 38:1–38:25, Apr. 2015.
  • B. Liu, Z. Wang, W. Zhu, Y. Sun, Z. Shen, L. Huang, Y. Li, Y. Gong, and W. Ge,   An ultra-low power always-on keyword spotting accelerator using quantized convolutional neural network and voltage-domain analog switching network-based approximate computing,   IEEE Access, vol. 7, pp. 186456–186469, 2019.
  • I. Khan, S. Choi, and Y.-W. Kwon,   Earthquake detection in a static and dynamic environment using supervised machine learning and a novel feature extraction method,   Sensors, vol. 20, no. 3, p. 800, Feb. 2020.
  • Q. Wang, P. Li, and Y. Kim, A parallel digital VLSI architecture for integrated support vector machine training and classification, IEEE Trans. Very Large Scale Integr. (VLSI) Syst., vol. 23, no. 8, pp. 1471–1484, Aug. 2015.
  • V. Gupta, D. Mohapatra, A. Raghunathan, and K. Roy,   Low-power digital signal processing using approximate adders,   IEEE Trans. Comput.-Aided Design Integr. Circuits Syst., vol. 32, no. 1, pp. 124–137, Jan. 2013.
  • Y. S. Yang and Y. Kim,   Approximate digital leaky Integrate-and-fire neurons for energy efficient spiking neural networks,   IEIE Trans. Smart Process. Comput., vol. 9, no. 3, pp. 252–259, Jun. 2020.
  • A. Raha, H. Jayakumar, and V. Raghunathan,   Input-based dynamic reconfiguration of approximate arithmetic units for video encoding,   IEEE Trans. Very Large Scale Integr. (VLSI) Syst., vol. 24, no. 3, pp. 846–857, Mar. 2016.
  • T. Moreau, A. Sampson, and L. Ceze,   Approximate computing: Making mobile systems more efficient,   IEEE Pervasive Comput., vol. 14, no. 2, pp. 9–13, Apr. 2015.
  • S. Mittal,   A survey of techniques for approximate computing,   ACM Comput. Surv., vol. 48, no. 4, pp. 1–33, May 2016.
    • " target="_blank">RIS     | Y. Kim, Y. Zhang, and P. Li,   A reconfigurable digital neuromorphic processor with memristive synaptic crossbar for cognitive computing,   ACM J. Emerg. Technol. Comput. Syst., vol. 11, no. 4, pp. 38:1–38:25, Apr. 2015.
  • B. Liu, Z. Wang, W. Zhu, Y. Sun, Z. Shen, L. Huang, Y. Li, Y. Gong, and W. Ge,   An ultra-low power always-on keyword spotting accelerator using quantized convolutional neural network and voltage-domain analog switching network-based approximate computing,   IEEE Access, vol. 7, pp. 186456–186469, 2019.
  • I. Khan, S. Choi, and Y.-W. Kwon,   Earthquake detection in a static and dynamic environment using supervised machine learning and a novel feature extraction method,   Sensors, vol. 20, no. 3, p. 800, Feb. 2020.
  • Q. Wang, P. Li, and Y. Kim, A parallel digital VLSI architecture for integrated support vector machine training and classification, IEEE Trans. Very Large Scale Integr. (VLSI) Syst., vol. 23, no. 8, pp. 1471–1484, Aug. 2015.
  • V. Gupta, D. Mohapatra, A. Raghunathan, and K. Roy,   Low-power digital signal processing using approximate adders,   IEEE Trans. Comput.-Aided Design Integr. Circuits Syst., vol. 32, no. 1, pp. 124–137, Jan. 2013.
  • Y. S. Yang and Y. Kim,   Approximate digital leaky Integrate-and-fire neurons for energy efficient spiking neural networks,   IEIE Trans. Smart Process. Comput., vol. 9, no. 3, pp. 252–259, Jun. 2020.
  • A. Raha, H. Jayakumar, and V. Raghunathan,   Input-based dynamic reconfiguration of approximate arithmetic units for video encoding,   IEEE Trans. Very Large Scale Integr. (VLSI) Syst., vol. 24, no. 3, pp. 846–857, Mar. 2016.
  • T. Moreau, A. Sampson, and L. Ceze,   Approximate computing: Making mobile systems more efficient,   IEEE Pervasive Comput., vol. 14, no. 2, pp. 9–13, Apr. 2015.
  • S. Mittal,   A survey of techniques for approximate computing,   ACM Comput. Surv., vol. 48, no. 4, pp. 1–33, May 2016.
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