Please note that the January session of this course has been rescheduled to June.
Discover how to build and utilize deep learning systems that extract meaningful information from large amounts of data. Over the course of two days, you’ll work closely with leading MIT experts to explore key trends in efficient processing techniques and learn to build custom hardware that makes deep learning relevant to your organization. You’ll leave better equipped to evaluate the rapidly growing number of deep learning hardware designs being proposed in academia and industry.
This course may be taken individually or as part of the Professional Certificate Program in Machine Learning & Artificial Intelligence.
Deep learning is widely used for many artificial intelligence (AI) applications including computer vision, speech recognition, robotics, etc. While deep learning delivers state-of-the-art accuracy on many AI tasks, it requires high computational complexity. Accordingly, designing efficient hardware systems to support deep learning is an important step towards enabling its wide deployment, particularly for embedded applications such as mobile, Internet of Things (IOT), and drones.
This course aims to provide a comprehensive tutorial and survey about the recent advances towards enabling the efficient processing of deep learning. Specifically, it will provide an overview of deep learning, discuss various hardware platforms and architectures that support deep learning, and highlight key trends in recent efficient processing techniques that reduce the cost of computation for deep learning either solely via hardware design changes or via joint hardware design and network algorithm changes. It will also summarize various development resources that can enable researchers and practitioners to quickly get started on deep learning design, and highlight important benchmarking metrics and design considerations that should be used for evaluating the rapidly growing number of deep learning hardware designs, optionally including algorithmic co-design, being proposed in academia and industry.
- Understand the basics of deep learning, how it is applied to various applications, and how it is processed on various platforms
- Outline the key design considerations for deep learning systems
- Be able to evaluate different deep learning implementations with benchmarks and comparison metrics
- Understand the strengths and weakness of various hardware architectures and platforms
- Be able to assess the utility of various design techniques for efficient processing for deep learning
- Understand and evaluate recent implementation trends and opportunities in deep learning systems
Who Should Attend
This course is designed for research scientists, engineers, developers, project managers, startups and investors/venture capitalists who work with or develop artificial intelligence for hardware and systems, as well as mobile or embedded applications:
- For project managers and investors/venture capitalists whose work involves assessing the viability or potential impact of a deep learning system and selecting a research direction or acquisition, this course aims to provide an overview of the recent trends as well as methods to assess the technical benefits and drawbacks of each approach or solution based on a comprehensive set of metrics.
- For research scientists and engineers whose work involves designing and building deep learning systems, this course aims to provide an overview of the various state-of-the-art techniques that are being used to address the challenges of building efficient deep learning systems.
- For startups and developers whose work involves developing deep learning algorithms and solutions for embedded applications and systems, this course aims to provide the insights necessary to select the best platform for your goals and needs. It will also highlight techniques that can be applied at the algorithm level to improve the energy-efficiency and speed of your proposed solution.
Day 1: 9:00 am - 4:30 PM
• Introduction / Course Overview
• Background on Deep Learning
• Deep Learning Applications
• Overview of Deep Neural Networks
Break [10:30-11:00 am]
• Popular Deep Neural Network Models
Break/Lunch [12:30-1:30 pm]
• Development Resources for Deep Learning
• Training Deep Neural Network Models
• Metrics for Evaluating Deep Learning Systems
Break [3:00-3:30 pm]
• Deep Learning on Programmable Platforms
• Discussion and Day 1 Summary
Day 2: 9:00 am - 4:30 PM
• Recap of Day 1 + Discussion
• Deep Learning on Specialized Hardware (P1)
Break [10:30-11:00 am]
• Deep Learning on Specialized Hardware (P2)
• Use of Advanced Technologies
Break/Lunch [12:30-1:30 pm]
• Co-optimization of Algorithms and Hardware
Short Break [3:00-3:30 pm]
• Discussion on Trends in Deep Learning and Day 2 Summary
Links & Resources
- People of ACM - Vivienne Sze, Association for Computing Machinery (ACM), September 22, 2020
- Shrinking deep learning’s carbon footprint, MIT News, August 7, 2020
- This chip was demoed at Jeff Bezos's secretive tech conference. It could be the key to the future of AI, Technology Review, May 1, 2019
- Vivienne Sze wins Edgerton Faculty Award, MIT News, April 17, 2019
- Reinventing the neural net chip for local analytics, ILP Institute Insider, August 14, 2018
- Interview: Vivienne Sze, associate professor of electrical engineering and computer science at MIT, insideBIGDATA.com, December 13, 2017
- Building the hardware for the next generation of artificial intelligence: Class taught by Vivenne Sze and Joel Elmer brings together traditionally separate disciplines for advances in deep learning. MIT News, November 30, 2017
- Bringing neural networks to cellphones. MIT News, July 18, 2017
- Energy-friendly chip can perform powerful artificial-intelligence tasks. MIT News, February 3, 2016
The type of content you will learn in this course, whether it's a foundational understanding of the subject, the hottest trends and developments in the field, or suggested practical applications for industry.
How the course is taught, from traditional classroom lectures and riveting discussions to group projects to engaging and interactive simulations and exercises with your peers.
What level of expertise and familiarity the material in this course assumes you have. The greater the amount of introductory material taught in the course, the less you will need to be familiar with when you attend.