An analog artificial neural network (ANN) classifier using a common-source amplifier based nonlinear activation function is presented in this work. 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.
ASJC Scopus subject areas
- Electrical and Electronic Engineering