An Energy Efficient Time-mode Digit Classification Neural Network Implementation


This paper presents the design of an ultra-low energy neural network that utilizes time-mode signal processing (TMSP). 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 analog- 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 0.6V. 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 2.37k

In Philosophical Transactions of the Royal Society A