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Joint optimisation of computational accuracy and algorithm parameters for energy-efficient recognition algorithms

Title
Joint optimisation of computational accuracy and algorithm parameters for energy-efficient recognition algorithms
Authors
Lim, H[Lim, Heesung]Park, T[Park, Taejoon]Kim, NS[Kim, Nam Sung]
DGIST Authors
Lim, H[Lim, Heesung]
Issue Date
2015-08-06
Citation
Electronics Letters, 51(16), 1238-1239
Type
Article
Article Type
Article
Keywords
Affect RecognitionAlgorithm ParametersAlgorithmsComputational AccuracyComputational EfficiencyEnergy EfficiencyEnergy EfficientEnergy UtilizationNeural NetworksOptimisationsParameter EstimationRecognition AccuracyRecognition AlgorithmTarget Recognition Algorithms
ISSN
0013-5194
Abstract
In this reported work, firstly, the artificial neural network (ANN) is taken as a target recognition algorithm and then jointly, the computational accuracy and an algorithm parameter (i.e. the number of hidden nodes) are optimised to minimise the overall energy consumption of ANN evaluations. This joint optimisation is motivated by the observation that both the computational accuracy and the algorithm parameter affect recognition accuracy and energy consumption. The evaluation shows that the jointly optimised computational accuracy and the algorithm parameter reduces the energy consumption of ANN evaluations by 79% at the same recognition target, compared with optimising only the algorithm parameter with precise computations. Furthermore, it is demonstrated that to evaluating ANNs with reduced computational accuracy, recognition accuracy is further improved by training the ANNs with reduced computational accuracy. This allows reduction of energy consumption by 86%. © The Institution of Engineering and Technology 2015.
URI
http://hdl.handle.net/20.500.11750/2867
DOI
10.1049/el.2015.0013
Publisher
Institution of Engineering and Technology
Files:
There are no files associated with this item.
Collection:
Information and Communication EngineeringETC1. Journal Articles


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