Syllabus:

Introduction to Deep Neural Networks(DNN), CNNs, DNN processing on GPUs, GPU input pipeline for DNN, Accelerators for DNNs, TPUs, DNN accelerators with emerging technologies, Accelerators for Graph Convolutional Networks (GCN), GCN accelerators with emerging technology,  Accelerators for Large Language Models(LLMs).

 Prerequisites:

Your interest in the subject 🙂

Grading policy:

The main focus of this course is on practical implementations, therefore No Exams for the Course 🙂

Following is the initial grading policy

Assignments                  >       30%

Quiz        >       20%

Preparing Notes > 10%

Class Participation >       10%

Project > 30%

The project consists of  3 phases:

Phase 1: Report (Due Date Mid Feb) > 5%

Phase 2: Report (Due Date Mid March) > 5%

phase 3: Presentation & Report (Due End of April) > 20%

Course Schedule:

Lectures by Industry Experts are written in BLUE

Academic Honesty:

As students of IISc, we expect you to adhere to the highest standards of academic honesty and integrity.

Elements of the course are designed to support your learning of the subject. Copying will not help you (in the exams or in the real world), so don't do it. If you have difficulties learning some of the topics or lack some background, try to form study groups where you can bounce off ideas with one another and try to teach each other what you understand. You're also welcome to talk to any of us and we'll be glad to help you.

If any exam/report is found to be copied, it will automatically result in a zero grade for that exam/project and a warning note to your advisor. Any repeat instance will automatically lead to a failing grade in the course.