Raw Data Library
About
Aims and ScopeAdvisory Board Members
More
Who We Are?
User Guide
Green Science
​
​
EN
Kurumsal BaşvuruSign inGet started
​
​

About
Aims and ScopeAdvisory Board Members
More
Who We Are?
User GuideGreen Science

Language

Kurumsal Başvuru

Sign inGet started
RDL logo

Verified research datasets. Instant access. Built for collaboration.

Navigation

About

Aims and Scope

Advisory Board Members

More

Who We Are?

Contact

Add Raw Data

User Guide

Legal

Privacy Policy

Terms of Service

Support

Got an issue? Email us directly.

Email: info@rawdatalibrary.netOpen Mail App
​
​

© 2026 Raw Data Library. All rights reserved.
PrivacyTermsContact
  1. Raw Data Library
  2. /
  3. Publications
  4. /
  5. Automated synthesis of a high-speed adder

Verified authors • Institutional access • DOI aware
50,000+ researchers120,000+ datasets90% satisfaction
Article
en
2025

Automated synthesis of a high-speed adder

0 Datasets

0 Files

en
2025
Vol 38 (3)
Vol. 38
DOI: 10.2298/fuee2503457b

Get instant academic access to this publication’s datasets.

Create free accountHow it works

Frequently asked questions

Is access really free for academics and students?

Yes. After verification, you can browse and download datasets at no cost. Some premium assets may require author approval.

How is my data protected?

Files are stored on encrypted storage. Access is restricted to verified users and all downloads are logged.

Can I request additional materials?

Yes, message the author after sign-up to request supplementary files or replication code.

Advance your research today

Join 50,000+ researchers worldwide. Get instant access to peer-reviewed datasets, advanced analytics, and global collaboration tools.

Get free academic accessLearn more
✓ Immediate verification • ✓ Free institutional access • ✓ Global collaboration
Access Research Data

Join our academic network to download verified datasets and collaborate with researchers worldwide.

Get Free Access
Institutional SSO
Secure
This PDF is not available in different languages.
No localized PDFs are currently available.
Padmanabhan Balasubramanian
Padmanabhan Balasubramanian

Institution not specified

Verified
Padmanabhan Balasubramanian
Nikos E. Mastorakis

Abstract

At the gate level, the Kogge-Stone adder (KSA) is known to outperform many high-speed adders including other parallel prefix adders in terms of the speed performance. This paper presents a methodology to synthesize a new high-speed adder automatically, called the AHSA, using a logic synthesis tool. We describe what adder architectures can be input to a logic synthesis tool and what synthesis constraints should be specified so that the AHSA can be automatically synthesized. The AHSA is significant since it has a speed similar to that of the KSA while requiring less area and dissipating less power. In this paper, 32-bit addition serves as an example, and various adders belonging to different architectures were synthesized using a 28 nm Synopsys CMOS standard cell library. The design metrics estimated show that while the KSA has a 5.2% reduced delay than the AHSA, the AHSA occupies 29.1% less area and consumes 9.6% less power than the KSA. In terms of the figures of merit used for a digital circuit design such as power-delay product (PDP), area-delay product (ADP), and power-delay-area product (PDAP), the AHSA achieves a 4.7% reduced PDP, a 25.2% reduced ADP, and a 32.4% reduced PDAP compared to the KSA. This paper demonstrates that when speed is the key factor in an adder design, the AHSA is preferable to the KSA. Moreover, the AHSA is shown to be significantly faster than other high-speed adders at the gate level.

How to cite this publication

Padmanabhan Balasubramanian, Nikos E. Mastorakis (2025). Automated synthesis of a high-speed adder. , 38(3), DOI: https://doi.org/10.2298/fuee2503457b.

Related publications

Why join Raw Data Library?

Quality

Datasets shared by verified academics with rich metadata and previews.

Control

Authors choose access levels; downloads are logged for transparency.

Free for Academia

Students and faculty get instant access after verification.

Publication Details

Type

Article

Year

2025

Authors

2

Datasets

0

Total Files

0

Language

en

DOI

https://doi.org/10.2298/fuee2503457b

Join Research Community

Access datasets from 50,000+ researchers worldwide with institutional verification.

Get Free Access