Skip to main content

Advertisement

Log in

Implementation and Investigation of an Optimal Full Adder Design for Low Power and Reduced Delay Conditions

  • Published:
Wireless Personal Communications Aims and scope Submit manuscript

Abstract

Full adder is one of the important components in electronics, used for various fundamental processing algorithms such as addition and multiplication. The application of these full adders is included in but not limited to Very Large-Scale Integration (VLSI) and Digital Signal Processing (DSP). To provide scalability and reliability to the advanced algorithms for high-end applications, the designing system of full adder should be enhanced. So, in this paper, we intended to improve the efficiency of a full adder circuit to work under low power and delay conditions. The software we used in this project is MENTOR GRAPHICS using 180 nm technology. The efficiency of the proposed transistor design is evaluated by analysing the power consumption, delay, PDP, capacitor load, delay w.r.t capacitance and PDP w.r.t capacitance. The parameters are compared between our proposed design and the literature schemes such as OLPFAD, DFEFA, DTLPCFA, and DPEHFA, respectively. It is evident that our proposed design outperforms the other.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9
Fig. 10
Fig. 11
Fig. 12
Fig. 13
Fig. 14
Fig. 15
Fig. 16
Fig. 17

Similar content being viewed by others

Data Availability

The data that support the findings of this study are available from the corresponding author, upon reasonable request.

Code availability

The code that is used this study are available from the corresponding author, upon reasonable request.

References

  1. Mandar, G., Rajiv, C., Nikhil, M., Siddhesh, J., Sangeeta, J. (2017). Design of 16-transistor Full Adder Circuit Using 6-transistor XNOR Gates. IEEE.

  2. Navi, K., Maeen, M., Foroutan, V., Timarchi, S., & Kavehei, O. (2009). A novel low-power full-adder cell for low voltage. Integration, 42(4), 457–467.

    Article  Google Scholar 

  3. Wang, D., Yang, M., Cheng, W., Guan, X., Zhu, Z., & Yang, Y. (2009). Novel low power full adder cells in 180nm CMOS technology. In: 2009 4th IEEE Conference on Industrial Electronics and Applications (pp. 430–433). IEEE.

  4. Shams, A. M., Darwish, T. K., & Bayoumi, M. A. (2002). Performance analysis of low-power 1-bit CMOS full adder cells. IEEE transactions on very large scale integration (VLSI) systems, 10(1), 20–29.

    Article  Google Scholar 

  5. Shipra, M., Shelendra Singh, T., Shyam A., (2013). Design low power 1OT full adder using process and circuit techniques. In: IEEE Proceedings of 7th international conference on intelligent systems and control (ISCO 2013). IEEE.

  6. Sudhanshu, J., Shaurakar, D., (2018). Design and performance evaluation of hybrid full adder for extensive PDP reduction. In: IEEE 2018 3rd international conference for convergence in technology (I2CT).

  7. Goel, S., Kumar, A., & Bayoumi, M. A. (2006). Design of robust, energy-efficient full adders for deep-submicrometer design using Hybrid-CMOS logic style. IEEE Transactions on Very Large-Scale Integration (VLSI) Systems, 14(12), 1309–1321.

    Article  Google Scholar 

  8. Moradi, F., Wisland, D. T., Mahmoodi, H., Aunet, S., Cao, T. V., & Peiravi, A. (2009). Ultra low power full adder topologies. In: 2009 IEEE International Symposium on Circuits and Systems (pp. 3158–3161). IEEE.

  9. Vijaya, L. A., Nagendra Babu, K. V. T., Sree Ram Deepak, M., Sai, K. A., Chandra Sekhar Yadav, G. V. P., Gopi, T. V., & Ghali, V. S. (2019). A machine learning based approach for defect detection and characterization in non-linear frequency modulated thermal wave imaging. International Journal of Emerging Trends in Engineering Research, 7(11), 517–522.

    Article  Google Scholar 

  10. Murthy, A. S., Murthy, P. S., Rajesh, V., Ahammad, S. H., & Jagan, B. O. (2019). Execution of natural random forest machine learning techniques on multi spectral image compression. International Journal of Pharmaceutical Research, 11(4), 1241–1255.

    Google Scholar 

  11. Sreedhar, B. S., & Bojja, P. (2019). Machine learning algorithms for MR brian image classification. International Journal of Recent Technology and Engineering, 8(3), 6744–6747.

    Google Scholar 

  12. Maddisetti, L., Senapati, R. K., & Ravindra, J. V. R. (2019). Supervised machine learning for training a neural network as 5:2 compressor. International Journal of Innovative Technology and Exploring Engineering, 8(10), 2079–208.

    Article  Google Scholar 

  13. Dudi, B., & Rajesh, V. (2019). Medicinal plant recognition based on CNN and machine learning. International Journal of Advanced Trends in Computer Science and Engineering, 8(4), 999–1003.

    Article  Google Scholar 

  14. Maddisetti, L., Senapati, R. K., & Ravindra, J. V. R. (2019). Training neural network as approximate 4:2 compressor applying machine learning algorithms for accuracy comparison. International Journal of Advanced Trends in Computer Science and Engineering, 8(2), 211–215.

    Article  Google Scholar 

  15. Raju, K., Pilli, S. K., Kumar, G. S. S., Saikumar, K., & Jagan, B. O. L. (2019). Implementation of natural random forest machine learning methods on multi spectral image compression. Journal of Critical Reviews, 6(5), 265–273.

    Google Scholar 

  16. Mittal S., Mittal V.K. (2019) Biomedical requirements for human machine interface towards building a humanoid: A review. In: 2019 IEEE 16th India council international conference, INDICON 2019 - Symposium Proceedings, pp. 1–6.

  17. Sripath, R. K., Roopkanth, K., Uday, T. V., Bhavana, V., & Priyanka, J. (2018). Student career prediction using advanced machine learning techniques. International Journal of Engineering and Technology (UAE), 7(2), 26–29.

    Article  Google Scholar 

  18. Danthala, S., Rao, S., Mannepalli, K., & Shilpa, D. (2018). Robotic manipulator control by using machine learning algorithms: A review. International Journal of Mechanical and Production Engineering Research and Development, 8(5), 305–310.

    Article  Google Scholar 

Download references

Funding

The author(s) received no financial support for the research, authorship, and/or publication of this article.

Author information

Authors and Affiliations

Authors

Contributions

Conceptualization: K. Praghash; Methodology: S Arunmetha; Formal analysis and investigation: K. Praghash & S. Arunmetha; Writing-original draft preparation: S. Arunmetha & K. Praghash, Writing-review and editing: B. Sai Tanuja, K. Preethi, N. P. N. S. Chandana.

Corresponding author

Correspondence to K. Praghash.

Ethics declarations

Conflict of interests

The author(s) have no conflicts of interest to declare.

Ethics approval

Not applicable.

Consent to participate

Not applicable.

Consent for publication

Not applicable.

Additional information

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Praghash, K., Arun Metha, S., Sai Tanuja, B. et al. Implementation and Investigation of an Optimal Full Adder Design for Low Power and Reduced Delay Conditions. Wireless Pers Commun 126, 3041–3069 (2022). https://doi.org/10.1007/s11277-022-09851-3

Download citation

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11277-022-09851-3

Keywords

Navigation