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Course information provided by the Courses of Study 2021-2022.
This Master's level course will take a hardware-centric view of machine learning systems. From constrained embedded microcontrollers to large distributed multi-GPU systems, we will investigate how these platforms run machine learning algorithms. We will look at different levels of the hardware/software/algorithm stack to make modern machine learning systems possible. This includes understanding different hardware acceleration paradigms, common hardware optimizations such as low-precision arithmetic and sparsity, compilation methodologies, model compression methods such as pruning and distillation, and multi-device federated and distributed training. Through hands-on assignments and an open-ended project, students will develop a holistic view of what it takes to train and deploy a deep neural network.
When Offered Spring.
Permission Note Enrollment limited to: Cornell Tech Students.
Prerequisites/Corequisites Prerequisite: Undergraduate ECE/CS degree, programming experience, introductory ML course.
- Understand how machine learning algorithms run on computer systems. This includes both the hardware and the software that maps computations onto the computer chips.
- Apply key optimization techniques such as pruning, quantization and distillation to machine learning algorithms to improve their efficiency on different hardware platforms.
- Analyze the performance and efficiency of different hardware platforms with and without optimizations, and understand the impact of efficiency optimizations on the accuracy of a machine learning algorithm.
- Design both the hardware and software components of a machine learning computer system.
Regular Academic Session.
Credits and Grading Basis
3 Credits Stdnt Opt(Letter or S/U grades)
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