COMMON SOURCE AMPLIFIER BASED MACHINE LEARNING ASSISTED IMAGE CLASSIFICATION
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An analog artificial neural network (ANN) classifier using a common-source amplifier based nonlinear activation function is presented in this thesis. A shallow ANN is designed using transistor level circuits and a multinomial (10 classes) classification accuracy of 0.82 is achieved on the MNIST dataset which consists of handwritten images of digits from 0-9. Use of common-source amplifier structure simplifies the ANN and results in 5X lower energy consumption than existing analog classifiers. The classifier performance is validated using Spectre and Matlab simulations.