This course provides in-depth coverage of the architectural techniques used to design accelerators for training and inference in machine learning systems. We start with classical ML algorithms including linear regression and support vector machines and mainly focus on DNN models such as convolutional neural nets and recurrent neural nets. The course will explore acceleration and hardware trade-offs for both training and inference of these models. We will also examine the impact of parameters including batch size, precision, sparsity and compression on the design space trade-offs for efficiency vs accuracy. The course presents several guest lecturers from top groups in industry and academia.
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