More and updated info at Hardware for Artificial Intelligence and Machine Learning

Offered:

Spring 2025

Course description:

Hardware (HW) is the foundation upon which artificial intelligence (AI) and machine learning (ML) systems are built. It provides the necessary computational power, efficiency, and flexibility to drive innovation in these emerging fields. By using HW/SW co-design, students will learn how to use, design, simulate, optimize, and evaluate specialized HW, such as GPUs, TPUs, FPGAs, and neuromorphic chips, for modern AI/ML algorithms. The intersection of HW and AI/ML is a rapidly growing field with significant career opportunities for computer engineers.

Course organization:

• The course is offered in-person only.
• There will be no course recordings.
• The course is organized into 18 lectures. Two of the lectures are dedicated for student presentations (mid-term and final project).

Learning outcomes:

• Understand the principles and tools for SW/HW co-design.
• Understand the foundations of neural networks.
• Understand the foundations of Large Language Models (LLMs).
• Understand the foundations of specialized hardware for AI/ML, such as GPUs, TPU, FPGAs, and neuromorphic architectures.
• Capable of mapping algorithms onto hardware.
• Capable of evaluating HW designs.
• Capable of optimizing HW designs through co-design for computational power, efficiency, and flexibility.
• Capable of using modern SW and HW tools for designing and using specialized HW for AI/ML.

Tentative course plan:

General catalog and banner information:

• Course prefix: ECE
• Course number: 410/510
• Catalog course title: Hardware for Artificial Intelligence and Machine Learning
• Credit hours: 4
• Grading option: Letter grade
• Course intended for: Graduate and undergraduate students
• Instructional method: Lecture
• Prerequisites (recommendations):
  • Undergraduate: ECE 371 and ECE 351
  • Graduate: ECE 485

More info:

Hardware for Artificial Intelligence and Machine Learning