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

Heesung Lim, Taejoon Park, Nam Sung Kim

Research output: Contribution to journalArticlepeer-review

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%.

Original languageEnglish (US)
Pages (from-to)1238-1240
Number of pages3
JournalElectronics Letters
Volume51
Issue number16
DOIs
StatePublished - Aug 6 2015

ASJC Scopus subject areas

  • Electrical and Electronic Engineering

Fingerprint Dive into the research topics of 'Joint optimisation of computational accuracy and algorithm parameters for energy-efficient recognition algorithms'. Together they form a unique fingerprint.

Cite this