Thesis F. Redaelli UIC Slides EN

T ask S cheduling T ailored for P artially D ynamically R econfigurable D evices UIC Thesis Defence May 2007 .:: Francesco Redaelli ::.

Outline ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]

Aims ,[object Object],[object Object],[object Object]

What’s next… ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]

Reconfiguration ,[object Object],[object Object]

Reconfiguration Partial Total Dynamic

Reconfiguration in everyday life ,[object Object],Hockey Football (Partial – Static)‏ (Complete – Static)‏ (Partial – Dynamic)‏

Problem Definition 1 of 2 ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]

Problem Definition 2 of 2 ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]

Reconfiguration Features ,[object Object],[object Object],[object Object],[object Object],[object Object]

Configuration Prefetching Ti: execution of task i Ri: configuration of task i

Module Reuse Ti: execution of task i Ri: configuration of task i

Anti-Fragmentation Techniques Ti: execution of task i Ri: configuration of task i

State Of the Art 1 of 3 ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]

State Of the Art 2 of 3 ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]

State Of the Art 3 of 3 ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]

ILP Formulation 1 of 2 ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]

ILP Formulation 2 of 2 OBJECTIVE FUNCTION ,[object Object],[object Object],[object Object],[object Object]

ILP Formulation – Constraints 1 of 5 1) Area constraint 2) Non overlap constraint 3) Right space

ILP Formulation – Constraints 2 of 5 4) Zero time 7) Always same column 8) Definition of on board time and offboard time

ILP Formulation – Constraints 3 of 5 9) Continuous usage 10) Reconfigured tasks constraints

ILP Formulation – Constraints 4 of 5 12) Permission of reuse 11) Single reconfiguration device 13) Reused task constraints

ILP Formulation – Constraints 5 of 5 14) Precedences 16) Final time constraint 15) Final time definition

Napoleon Reconfiguration-Aware Scheduler 1 of 3 ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]

Napoleon Reconfiguration-Aware Scheduler 2 of 3 ,[object Object],[object Object],Ti: execution of task i Ri: configuration of task i

[object Object],Ti: execution of task i Ri: configuration of task i Napoleon Reconfiguration-Aware Scheduler 3 of 3

Experimental Results ,[object Object],[object Object],[object Object],[object Object]

Other Algorithms ,[object Object],[object Object],[object Object],These algorithms have been used to verify the real effectiveness of Napoleon They have been chosen as the most representative for the solution of our problem in the literature When a deconfiguration policy is used it is unlimited All these algorithms have been implemented ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]

Conclusions and Future Works ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]

Thesis F. Redaelli UIC Slides EN

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Thesis F. Redaelli UIC Slides EN