Publication detail

Fast Reconfigurable Hash Functions for Network Flow Hashing in FPGAs

GROCHOL, D. SEKANINA, L.

Original Title

Fast Reconfigurable Hash Functions for Network Flow Hashing in FPGAs

Type

conference paper

Language

English

Original Abstract

Efficient monitoring of high speed computer networks operating with a 100 Gigabit per second (Gbps) data throughput requires a suitable hardware acceleration of its key components. We present a platform capable of automated design of hash functions suitable for network flow hashing. The platform employs a multi-objective linear genetic programming developed for the hash function design. We evolved high-quality hash functions and implemented them in a field programmable gate array (FPGA). Several evolved hash functions were combined together in order to form a new reconfigurable hash function. The proposed reconfigurable design significantly reduces the area on a chip while the maximum operation frequency remains very close to the fastest hash functions. Properties of evolved hash functions were compared with the state-of-the-art hash functions in terms of the quality of hashing, area and operation frequency in the FPGA.

Keywords

hash function, FPGA, genetic programming, network flow

Authors

GROCHOL, D.; SEKANINA, L.

Released

15. 6. 2018

Publisher

Institute of Electrical and Electronics Engineers

Location

Edinburgh

ISBN

978-1-5386-7753-7

Book

Proceedings of the 2018 NASA/ESA Conference on Adaptive Hardware and Systems

Pages from

257

Pages to

263

Pages count

7

URL

https://www.fit.vut.cz/research/publication/11706/

BibTex

@inproceedings{BUT155031,
  author="David {Grochol} and Lukáš {Sekanina}",
  title="Fast Reconfigurable Hash Functions for Network Flow Hashing in FPGAs",
  booktitle="Proceedings of the 2018 NASA/ESA Conference on Adaptive Hardware and Systems",
  year="2018",
  pages="257--263",
  publisher="Institute of Electrical and Electronics Engineers",
  address="Edinburgh",
  doi="10.1109/AHS.2018.8541401",
  isbn="978-1-5386-7753-7",
  url="https://www.fit.vut.cz/research/publication/11706/"
}

Documents

ahs19_hash.pdf
08541401.pdf