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EEC 282

— Machine Learning Systems & Hardware

(4 units)

Hardware architectures and systems for executing machine-learning workloads, with emphasis on dataflows, memory hierarchy, accelerator microarchitecture, hardware quantization, hardware with support for model sparsity, and hardware–software co-design.

Prerequisites & course information

Course Description: Hardware architectures and systems for executing machine-learning workloads, with emphasis on dataflows, memory hierarchy, accelerator microarchitecture, hardware quantization, hardware with support for model sparsity, and hardware–software co-design. Learning Activities: Lecture 4 hour(s). Grade Mode: Letter. Learning Activities: Lecture 4 hour(s). Grade Mode: Letter.

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