Abstract
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 language | English |
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Pages (from-to) | 1-15 |
Number of pages | 15 |
Journal | Philosophical transactions. Series A, Mathematical, physical, and engineering sciences |
Volume | 378 |
Issue number | 2164 |
DOIs | |
Publication status | Published - 2020 |
Bibliographical note
Accepted Author ManuscriptKeywords
- classification
- energy efficiency
- handwritten digit
- neural network
- time-mode
- ultra-low energy