Tuyển tập báo cáo các nghiên cứu khoa học quốc tế ngành hóa học dành cho các bạn yêu hóa học tham khảo đề tài: Research Article OLLAF: A Fine Grained Dynamically Reconﬁgurable Architecture for OS Support | Hindawi Publishing Corporation EURASIP Journal on Embedded Systems Volume 2009 Article ID 574716 11 pages doi 2009 574716 Research Article OLLAF A Fine Grained Dynamically Reconfigurable Architecture for OS Support Samuel Garcia and Bertrand Granado ETIS Laboratory CNRS UMR8051 University of Cergy-Pontoise ENSEA 6 Avenue du Ponceau F 95000 Cergy-Pontoise France Correspondence should be addressed to Samuel Garcia Received 15 March 2009 Revised 24 June 2009 Accepted 22 September 2009 Recommended by Markus Rupp Fine Grained Dynamically Reconfigurable Architecture FGDRA offers a flexibility for embedded systems with a great power processing efficiency by exploiting optimizations opportunities at architectural level thanks to their fine configuration granularity. But this increase design complexity that should be abstracted by tools and operating system. In order to have a usable solution a good inter-overlapping between tools OS and platform must exist. In this paper we present OLLAF an FGDRA specially designed to efficiently support an OS. The studies presented here show the contribution of this architecture in terms of hardware context management and preemption support. Studies presented here show the gain that can be obtained by using OLLAF instead of a classical FPGA in terms of context management and preemption overhead. Copyright 2009 S. Garcia and B. Granado. This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use distribution and reproduction in any medium provided the original work is properly cited. 1. Introduction Many modern applications for example robots navigation have a dynamic behavior but the hardware targets today are still static and this dynamic behavior is managed in software. This management is lowering the computation performances in terms of time and expressivity. To obtain best performances we need a dynamical computing paradigm. This paradigm .