<p>Lecture, four hours; outside study, eight hours. Recommended requisites: courses 115C, M116C, C147A. Introduction to hardware-aware machine learning (ML) with applications in computer vision, natural language, and big data. Students become better users and developers of artificial intelligence (AI) chips with software and hardware co-optimization, considering applications, algorithms, microarchitectures, circuits, and technologies for AI computing. Topics include neural network compression; operators, dataflows, and exemplar ML accelerator architectures; distributed training/inference; emerging computing models for AI. Example project topics include network compression, accelerator architecture modeling, hardware impact modeling of distributed inference. Letter grading.</p>

Electrical and Computer Engineering

Henry Samueli School of Engineering and Applied Science

EC ENGR 201C - Artificial Intelligence on Chip

Artificial Intelligence on Chip

Ability to compress neural networks for execution on resource- constrained hardware.

 Ability to read research literature and present it.

Understand the impact of data flows and model parallelization techniques.

Ability to model AI hardware systems in terms of performance and power.

General Catalog

Artificial Intelligence on Chip

EC ENGR 201C

4 units