July 07 - 08, 2025
On Campus and Live Online
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.
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
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.
July 07 - 08, 2025
Live Online
2 days
$2,500
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
Vivienne Sze is an associate professor in MIT’s Department of Electrical Engineering and Computer Science and leads the Research Lab of Electronics’ Energy-Efficient Multimedia Systems research group. Her group works on computing systems that enable energy-efficient machine learning, computer vision, and video compression/processing for a wide range of applications, including autonomous navigation, digital health, and the Internet of Things.
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