EC ENGR 201C · EE 201CArtificial Intelligence on Chip
Electrical and Computer Engineering · 4 units · Graduate courses (200-299)
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.
When it runs
- Fall 2026
Scheduled, not typical — from UCLA’s Schedule of Classes, which publishes Fall 2025 through Spring 2027 and nothing before it.
Requisites
Official UCLA wording
Recommended requisites: courses 115C, M116C, C147A.
BruinTree reads · Recommended
confidence 1.00 · from textRequires
Everything that has to come before this course, not just the courses named in the requisite above.
EC ENGR 201C
- EC ENGR 115CDigital Electronic Circuits
- EC ENGR 100Electrical and Electronic Circuits
- MECH&AE 82Mathematics of Engineering
- PHYSICS 1CPhysics for Scientists and Engineers: Electrodynamics, Optics, and Special Relativity3 more beneath
- EC ENGR 115AAnalog Electronic Circuits I
- COM SCI M51ALogic Design of Digital Systems
- EC ENGR M116CComputer Systems Architecture
- EC ENGR M16Logic Design of Digital Systems
- COM SCI 33Introduction to Computer Organization
- COM SCI 32Introduction to Computer Science II
- EC ENGR C147ANeural Networks and Deep Learning
- EC ENGR 131AProbability and Statistics
- EC ENGR 133AApplied Numerical Computing
- C&EE M20Introduction to Computer Programming with MATLAB
- MECH&AE M20Introduction to Computer Programming with MATLAB
- COM SCI 31Introduction to Computer Science I
- EC ENGR M146Introduction to Machine Learning
- STATS 100AIntroduction to Probability
- MATH 170EIntroduction to Probability and Statistics 1: Probability
- EC ENGR 205AMatrix Analysis for Scientists and Engineers
3 direct requisites. Showing 33 courses over 3 levels; the branches marked with a count carry on past it. Every course here opens its own tree.
Unlocks
What this course is a requisite for, and what those courses lead to in turn.
No course in the catalog lists this as a requisite.