A parallel algorithm for scheduling problem based on Hopfield model for the automated synthesis of digital systems

Mehrdad Nourani-Dargiri, Christos A. Papachristou, Yoshiyasu Takefuji

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

Summary form only given. A novel scheduling approach has been developed based on the deterministic Hopfield model for high-level synthesis. The model uses a four-dimensional neural network architecture to schedule the operations of a dataflow graph and maps them to specific functional units. Neural network-based scheduling is achieved by formulating the scheduling problem in terms of an energy function and by using the motion equation corresponding to the variation of energy. The algorithm searches the scheduling space in parallel and finds the optimal schedule. The main contribution of the present work is an efficient scheduling algorithm under time and resource constraints. The algorithm is based on moves in the scheduling space, which correspond to moves towards the equilibrium point (lowest energy state) in the dynamic system space. The neurons' motion equation is the heart of this guided movement mechanism and guarantees that the state of the system always converges to the lowest energy state.

Original languageEnglish
Title of host publicationProceedings. IJCNN - International Joint Conference on Neural Networks
Editors Anon
PublisherPubl by IEEE
Number of pages1
ISBN (Print)0780301641
Publication statusPublished - 1992 Jan 1
EventInternational Joint Conference on Neural Networks - IJCNN-91-Seattle - Seattle, WA, USA
Duration: 1991 Jul 81991 Jul 12

Publication series

NameProceedings. IJCNN - International Joint Conference on Neural Networks

Other

OtherInternational Joint Conference on Neural Networks - IJCNN-91-Seattle
CitySeattle, WA, USA
Period91/7/891/7/12

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ASJC Scopus subject areas

  • Engineering(all)

Cite this

Nourani-Dargiri, M., Papachristou, C. A., & Takefuji, Y. (1992). A parallel algorithm for scheduling problem based on Hopfield model for the automated synthesis of digital systems. In Anon (Ed.), Proceedings. IJCNN - International Joint Conference on Neural Networks (Proceedings. IJCNN - International Joint Conference on Neural Networks). Publ by IEEE.