### 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 language | English |
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Title of host publication | Proceedings. IJCNN - International Joint Conference on Neural Networks |

Editors | Anon |

Publisher | Publ by IEEE |

Pages | 910 |

Number of pages | 1 |

ISBN (Print) | 0780301641 |

Publication status | Published - 1992 |

Externally published | Yes |

Event | International Joint Conference on Neural Networks - IJCNN-91-Seattle - Seattle, WA, USA Duration: 1991 Jul 8 → 1991 Jul 12 |

### Other

Other | International Joint Conference on Neural Networks - IJCNN-91-Seattle |
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City | Seattle, WA, USA |

Period | 91/7/8 → 91/7/12 |

### Fingerprint

### ASJC Scopus subject areas

- Engineering(all)

### Cite this

*Proceedings. IJCNN - International Joint Conference on Neural Networks*(pp. 910). Publ by IEEE.

**A parallel algorithm for scheduling problem based on Hopfield model for the automated synthesis of digital systems.** / Nourani-Dargiri, Mehrdad; Papachristou, Christos A.; Takefuji, Yoshiyasu.

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

*Proceedings. IJCNN - International Joint Conference on Neural Networks.*Publ by IEEE, pp. 910, International Joint Conference on Neural Networks - IJCNN-91-Seattle, Seattle, WA, USA, 91/7/8.

}

TY - GEN

T1 - A parallel algorithm for scheduling problem based on Hopfield model for the automated synthesis of digital systems

AU - Nourani-Dargiri, Mehrdad

AU - Papachristou, Christos A.

AU - Takefuji, Yoshiyasu

PY - 1992

Y1 - 1992

N2 - 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.

AB - 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.

UR - http://www.scopus.com/inward/record.url?scp=0026762023&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=0026762023&partnerID=8YFLogxK

M3 - Conference contribution

AN - SCOPUS:0026762023

SN - 0780301641

SP - 910

BT - Proceedings. IJCNN - International Joint Conference on Neural Networks

A2 - Anon, null

PB - Publ by IEEE

ER -