Please use this identifier to cite or link to this item: https://hdl.handle.net/20.500.14279/13942
Title: Optimizing SHA-1 hash function for high throughput with a partial unrolling study
Authors: Michail, Harris 
Kakarountas, A. P. 
Selimis, George N. 
Goutis, Costas E.
Major Field of Science: Engineering and Technology
Field Category: Electrical Engineering - Electronic Engineering - Information Engineering
Keywords: Computer software;Energy dissipation;Document imaging systems
Issue Date: Sep-2005
Source: (2005) Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 3728 LNCS, pp. 591-600; 15th International Workshop on Integrated Circuit and System Design: Power and Timing Modeling, Optimization and Simulation, PATMOS 2005; Leuven; Belgium; 20 September 2005 through 23 September 2005
Volume: 3728 LNCS
Conference: International Workshop on Integrated Circuit and System Design: Power and Timing Modeling, Optimization and Simulation 
Abstract: Hash functions are widely used in applications that call for data integrity and signature authentication at electronic transactions. A hash function is utilized in the security layer of every communication protocol. As time passes more sophisticated applications arise that address to more users-clients and thus demand for higher throughput. Furthermore, due to the tendency of the market to minimize devices' size and increase their autonomy to make them portable, power issues have also to be considered. The existing SHA-1 Hash Function implementations (SHA-1 is common in many protocols e.g. IPSec) limit throughput to a maximum of 2 Gbps. In this paper, a new implementation comes to exceed this limit improving the throughput by 53%. Furthermore,power dissipation is kept low compared to previous works, in such way that the proposed implementation can be characterized as low-power. © Springer-Verlag Berlin Heidelberg 2005.
Description: Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), Volume 3728, 2005, Pages 591-600
ISBN: 978-3-540-29013-1 (print)
ISSN: 978-3-540-32080-7 (online)
DOI: 10.1007/11556930_60
Rights: © 2005 Springer
Type: Conference Papers
Affiliation : University of Patras 
Publication Type: Peer Reviewed
Appears in Collections:Δημοσιεύσεις σε συνέδρια /Conference papers or poster or presentation

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