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Joint optimisation of computational accuracy and algorithm parameters for energy-efficient recognition algorithms
- Joint optimisation of computational accuracy and algorithm parameters for energy-efficient recognition algorithms
- Lim, H[Lim, Heesung]; Park, T[Park, Taejoon]; Kim, NS[Kim, Nam Sung]
- DGIST Authors
- Lim, H[Lim, Heesung]
- Issue Date
- Electronics Letters, 51(16), 1238-1239
- Article Type
- Affect Recognition; Algorithm Parameters; Algorithms; Computational Accuracy; Computational Efficiency; Energy Efficiency; Energy Efficient; Energy Utilization; Neural Networks; Optimisations; Parameter Estimation; Recognition Accuracy; Recognition Algorithm; Target Recognition Algorithms
- 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.
- Institution of Engineering and Technology
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