An energy efficient time-mode digit classification neural network implementation

O.C. Akgün, J. Mei

Research output: Contribution to journalArticleScientificpeer-review

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This paper presents the design of an ultra-low energy neural network that uses time-mode signal processing). Handwritten digit classification using a single-layer artificial neural network (ANN) with a Softmin-based activation function is described as an implementation example. To realize time-mode operation, the presented design makes use of monostable multivibrator-based multiplying analogue-to-time converters, fixed-width pulse generators and basic digital gates. The time-mode digit classification ANN was designed in a standard CMOS 0.18 μm IC process and operates from a supply voltage of 0.6 V. The system operates on the MNIST database of handwritten digits with quantized neuron weights and has a classification accuracy of 88%, which is typical for single-layer ANNs, while dissipating 65.74 pJ per classification with a speed of 2.37 k classifications per second. This article is part of the theme issue 'Harmonizing energy-autonomous computing and intelligence'.

Original languageEnglish
Pages (from-to)1-15
Number of pages15
JournalPhilosophical transactions. Series A, Mathematical, physical, and engineering sciences
Issue number2164
Publication statusPublished - 2020


  • classification
  • energy efficiency
  • handwritten digit
  • neural network
  • time-mode
  • ultra-low energy

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