NDLI: Multilevel adaptive neural network architecture for implementing single-chip intelligent agents on FPGAs

Content Provider	IEEE Xplore Digital Library
Author	Finker, R. del Campo, I. Echanobe, J. Doctor, F.
Copyright Year	2013
Description	Author affiliation: Fac. of Eng. & Comput., Coventry Univ., Coventry, UK (Doctor, F.) \|\| Dept. of Electr. & Electron., Univ. of the Basque Country, Leioa, Spain (Finker, R.; del Campo, I.; Echanobe, J.)
Abstract	The powerful synergy of neural networks and reconfigurable hardware provides a solid foundation for the development of high performance embedded systems able to efficiently adapt to changing requirements. Adaptation at different levels - ranging from the physical level to the system level-can be combined to develop efficient solutions by means of FPGA technology. In this work, a multilevel adaptation scheme for the development of intelligent agents is proposed. Software learning algorithms are applied to adapt the agent behavior (i.e. neural network parameters) at the system level, while dynamic partial reconfiguration (DPR) is used to modify the agent at the physical and architectural level (i.e. neural network topology). Firstly, a multilevel adaptive intelligent agent is able to manage its resources efficiently in order to meet time-varying demands such as speed performance and power consumption. Secondly, from the behavioral viewpoint, multilevel adaptation provides the intelligent agent with high plasticity and flexibility. An FPGA-based intelligent agent has been successfully deployed for a real-time control problem in an inhabited intelligent environment. Results obtained show that the agent is able to adapt itself to changes in the environment in a lifelong mode.
Starting Page	1
Ending Page	9
File Size	351245
Page Count	9
File Format	PDF
ISSN	21614407
e-ISBN	9781467361293
DOI	10.1109/IJCNN.2013.6706760
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2013-08-04
Publisher Place	USA
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Neurons Topology Artificial neural networks Intelligent agents Hardware Training Network topology
Content Type	Text
Resource Type	Article

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