This course explores the design, programming, and performance of modern AI accelerators. It covers architectural techniques, dataflow, tensor processing, memory hierarchies, compilation for accelerators, and emerging trends in AI computing. This course will cover modern AI/ML algorithms such as convolutional neural nets, and Transformer-based models / LLMs. We will consider both training and inference for these models and discuss the impact of parameters such as batch size, precision, sparsity and compression on the accuracy of these models. Students will become familiar with hardware implementation techniques for using parallelism, locality, and low precision to implement the core computational kernels used in ML. Students will develop intuitions to make system-level trade-offs to design energy-efficient accelerators. Students will read recent research papers and complete a final design project.
3-4 units · Letter or Credit/No Credit
This course explores the design, programming, and performance of modern AI accelerators. It covers architectural techniques, dataflow, tensor processing, memory hierarchies, compilation for accelerators, and emerging trends in AI computing. This course will cover modern AI/ML algorithms such as convolutional neural nets, and Transformer-based models / LLMs. We will consider both training and inference for these models and discuss the impact of parameters such as batch size, precision, sparsity and compression on the accuracy of these models. Students will become familiar with hardware implementation techniques for using parallelism, locality, and low precision to implement the core computational kernels used in ML. Students will develop intuitions to make system-level trade-offs to design energy-efficient accelerators. Students will read recent research papers and complete a final design project.
Offered in Spring 2027 at Stanford University.