Spring 2021: CS/ECE 7710 Neuromorphic Architectures

General Information:

• Venue: IVC, i.e., class will meet at regular times over Zoom (see link on Canvas)
• Time: Tuesday, Thursday 2-3:20pm
• Instructor: Rajeev Balasubramonian, email: rajeev@cs, MEB 3414, office hours: immediately following each lecture, or email to set up an appointment.
• Pre-Requisite: None. Background in architecture and machine learning will be helpful, but is not required. The material presented in class will be self-contained.

Course Description:

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.

University Support:

College of Engineering Policies (Disability, Add, Drop, Appeals, Safety, etc.).

School of Computing Policies

Class rosters are provided to the instructor with the student's legal name as well as "Preferred first name" (if previously entered by you in the Student Profile section of your CIS account). While CIS refers to this as merely a preference, I will honor you by referring to you with the name and pronoun that feels best for you in class, on papers, exams, group projects, etc. Please advise me of any name or pronoun changes (and please update CIS) so I can help create a learning environment in which you, your name, and your pronoun will be respected.

Grading:

Tentative Class Schedule