Fall 2019: Brain-Inspired Computing: Models, Architectures, and Programming

Brain-inspired computing is a subset of AI-based machine learning and is generally referred to both deep and shallow artificial neural networks (ANN) and spiking neural networks (SNN). Deep convolutional neural networks (CNN) have made pervasive market inroads in numerous commercial applications and their software implementations are widely studied in computer vision, speech processing and other courses. The purpose of this course will be to study the wide gamut of shallow and deep neural network models, the methodologies for specialized hardware design of popular learning algorithms, as well as adapting hardware architectures on crossbar fabrics of emerging technologies such as memristors and spin torque nonmagnetic devices. Existing software development tools such as TensorFlow, Caffe, and PyTorch will be leveraged to teach various aspects of neuromorphic designs.