Paper Info
Title
Mapping Adaptive Particle Filters to Heterogeneous Reconfigurable Systems
Abstract
This article presents an approach for mapping real-time applications based on particle filters (PFs) to heterogeneous reconfigurable systems, which typically consist of multiple FPGAs and CPUs. A method is proposed to adapt the number of particles dynamically and to utilise runtime reconfigurability of FPGAs for reduced power and energy consumption. A data compression scheme is employed to reduce communication overhead between FPGAs and CPUs. A mobile robot localisation and tracking application is developed to illustrate our approach. Experimental results show that the proposed adaptive PF can reduce up to 99% of computation time. Using runtime reconfiguration, we achieve a 25% to 34% reduction in idle power. A 1U system with four FPGAs is up to 169 times faster than a single-core CPU and 41 times faster than a 1U CPU server with 12 cores. It is also estimated to be 3 times faster than a system with four GPUs.
Year
DOI
Venue
2015
10.1145/2629469
TRETS
Keywords
Field
DocType
algorithms,design,heterogeneous systems,sequential monte carlo,reconfigurable systems,fpgas,performance,particle filters,real-time and embedded systems,runtime reconfiguration
Reconfigurability,Computer science,Parallel computing,Particle filter,Field-programmable gate array,Real-time computing,Data compression,Energy consumption,Control reconfiguration,Mobile robot,Computation,Embedded system
Journal
Volume
Issue
ISSN
7
4
1936-7406
Citations 
PageRank 
References 
2
0.38
11
Authors
6
Name
Order
Citations
PageRank
Thomas C. P. Chau1536.81
Xinyu Niu213523.16
Alison Eele3202.36
Jan M. Maciejowski433638.92
Peter Y. K. Cheung51720208.45
Wayne Luk63752438.09