Course Detail
Course Components:
The course will cover hardware approaches for implementing neural-inspired algorithms. In recent years, machine learning and AI have re-emerged as effective solutions to a number of difficult and economically relevant problems. They will likely enable autonomous vehicles, healthcare solutions, assistive technologies, etc. These solutions will be deployed in datacenters, mobile phones, self-driving cars, and sensors. The course will start with a brief primer on why machine learning has made significant strides in the past decade. We will then move to discussing specialized processors (accelerators) that can efficiently execute a large family of machine learning algorithms (for both inference and training). We will focus our discussions on accelerators for artificial/spiking neural networks, and convolutional neural networks -- areas that have dominated recent architecture conferences. We will end the course by discussing how the learned concepts can apply to other relevant application domains, e.g., genomic analysis. The course does not have any formal pre-requisites, but is intended primarily for graduate students with some familiarity in architecture and/or machine learning. The lectures will be self-contained, i.e., I will provide sufficient background in architecture and machine learning to make the material accessible. Most class lectures will be based on recent research papers (see tentative schedule below). Students will also work in groups on semester-long projects -- the projects will compare the implementations of various cognitive tasks with different algorithms and hardware approaches.