This course is offered in a hybrid format, with in-person and live online cohorts attending simultaneously. When registering, select the appropriate registration button below.
Autonomous robots. Self-driving cars. Smart refrigerators. Now embedded in countless applications, deep learning provides unparalleled accuracy relative to previous AI approaches.
Yet, cutting through computational complexity and developing custom hardware to support deep learning can prove challenging for many enterprises—and the cost of getting it wrong can be catastrophic.
Do you have the advanced knowledge you need to keep pace in the deep learning revolution?
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.
Put the smart in your hardware
In this intensive two-day course, you’ll receive a high-level overview of deep learning, discuss various hardware platforms and architectures that support deep learning, and explore key trends in recent efficient processing techniques that reduce the cost of computation for deep learning. Professor Vivienne Sze 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.
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.
Certificate of Completion from MIT Professional Education
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.
- Fundamentals: Core concepts, understandings, and tools - 40%
- Latest Developments: Recent advances and future trends - 30%
- Industry Applications: Linking theory and real-world - 30%
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.
- Lecture: Delivery of material in a lecture format - 70%
- Discussions or Group Work: Participatory learning - 20%
- Labs: Demonstrations, experiments, simulations - 10%
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.
- Introductory: Appropriate for a general audience - 50%
- Specialized: Assumes experience in practice area or field - 30%
- Advanced: In-depth exploration at the graduate level - 20%