An energy efficient time-mode digit classification neural network implementation
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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'.