TY - GEN
T1 - An online task placement algorithm based on maximum empty rectangles in dynamic partial reconfigurable systems
AU - Wang, Guohua
AU - Liu, Song
AU - Nie, Jing
AU - Wang, Fengzhou
AU - Arslan, Tughrul
PY - 2017/9/19
Y1 - 2017/9/19
N2 - Dynamic Partial Reconfigurable Systems can significantly improve the utilization of hardware by performing multiple tasks on the reconfigurable resources in run-Time, whereas the limited reconfigurable resources constraint the number of tasks. To achieve efficient operation of systems, it is crucial to increase the utilization of resources while allocating tasks. This paper proposes a novel MER-based heuristic based on 3D-Adjacency heuristic, trying to reduce the area fragmentation, to improve the utilization rate and to deal with the problem that more than one task may arrive at the same time. It is a new attempt to combine MER technique with adjacency heuristic. Meanwhile, this paper also introduces an innovative task placement algorithm which taking into account the types of resources (like CLBs, RAMs and so on) and their relative positions that tasks contain. Simulation experiment results indicate that our methods have higher resource utilization, a higher task acceptance ratio, and a lower fragmentation ratio compared with some existing methods. Our algorithm can improve 17% to 26% in terms of task acceptance ratio compared to conventional algorithms.
AB - Dynamic Partial Reconfigurable Systems can significantly improve the utilization of hardware by performing multiple tasks on the reconfigurable resources in run-Time, whereas the limited reconfigurable resources constraint the number of tasks. To achieve efficient operation of systems, it is crucial to increase the utilization of resources while allocating tasks. This paper proposes a novel MER-based heuristic based on 3D-Adjacency heuristic, trying to reduce the area fragmentation, to improve the utilization rate and to deal with the problem that more than one task may arrive at the same time. It is a new attempt to combine MER technique with adjacency heuristic. Meanwhile, this paper also introduces an innovative task placement algorithm which taking into account the types of resources (like CLBs, RAMs and so on) and their relative positions that tasks contain. Simulation experiment results indicate that our methods have higher resource utilization, a higher task acceptance ratio, and a lower fragmentation ratio compared with some existing methods. Our algorithm can improve 17% to 26% in terms of task acceptance ratio compared to conventional algorithms.
KW - Dynamic Partial Reconfigurable System
KW - resource management
KW - task placement
UR - http://www.scopus.com/inward/record.url?scp=85032911713&partnerID=8YFLogxK
U2 - 10.1109/AHS.2017.8046376
DO - 10.1109/AHS.2017.8046376
M3 - Conference contribution
AN - SCOPUS:85032911713
T3 - 2017 NASA/ESA Conference on Adaptive Hardware and Systems, AHS 2017
SP - 180
EP - 185
BT - 2017 NASA/ESA Conference on Adaptive Hardware and Systems, AHS 2017
PB - Institute of Electrical and Electronics Engineers
T2 - 2017 NASA/ESA Conference on Adaptive Hardware and Systems, AHS 2017
Y2 - 24 July 2017 through 27 July 2017
ER -