JOURNAL ARTICLE
High-Performance Hardware Implementation of the KATAN Lightweight Cryptographic Cipher.
Published In: Journal of Circuits, Systems & Computers, 2023, v. 32, n. 1. P. 1 1 of 3
Database: Academic Search Ultimate 2 of 3
Authored By: Al-Moselly, Muntaser; Al-Haj, Ali 3 of 3
Abstract
Lightweight cryptography has been proposed recently as an attractive solution to provide security for the ever-growing number of IoT resource-constrained devices. Many of the proposed lightweight cryptographic ciphers have been implemented in software. However, for practical embedded IoT applications, hardware implementations are preferred because they have small silicon area and low-power consumption. In this paper, we present a transistor-level hardware implementation of the well-known KATAN lightweight cipher. This cipher has been chosen due to its operational simplicity and high levels of security. Moreover, the structure of the KATAN cipher lends itself naturally for transistor-level hardware implementation. The design has been implemented at the transistor level using the advanced new 28-nm CMOS technology which facilitates optimized designs for the resource-constrained IoT devices. The proposed VLSI KATAN encryption and decryption circuits have been designed and simulated using the Synopsys Custom Designer Tool using 28-nm technology, 0.9 v supply voltage and a 1 GHz clock signal. The KATAN encryption circuit has 312 GE (Gate Equivalent) without key and irregular update registers, and 1081 GE for the overall design, and the decryption circuit has 390 GE without memory registers and 6867 GE for the overall design. [ABSTRACT FROM AUTHOR]
Additional Information
- Source:Journal of Circuits, Systems & Computers. 2023/01, Vol. 32, Issue 1, p1
- Document Type:Article
- Subject Area:Communication and Mass Media
- Publication Date:2023
- ISSN:0218-1266
- DOI:10.1142/S0218126623500172
- Accession Number:161163008
- Copyright Statement:Copyright of Journal of Circuits, Systems & Computers is the property of World Scientific Publishing Company and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)
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