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Developing Embedded AI Systems

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Overview

Embedded artificial intelligence (AI) will enable new, inexpensive, and low power AI solutions that are not possible using cloud-based AI technologies alone. The edge AI chip market is growing much faster than the overall chip market, with the number of edge AI chips to be sold in 2024 estimated to be 1.5 billion. Embedded AI requires knowledge and skills beyond traditional embedded systems, data science, and machine learning (ML). It requires knowledge of devices, sensors, and advanced, near real-time signal processing methods for video, audio, motion, or other signals. Specialized software tools and frameworks are required to develop embedded AI applications.

This program provides the knowledge and skills required to take advantage of this next major shift in technologies and the related growth in job demand. The program explores the specialized tools, frameworks, technologies, platforms, and methods used to create exciting new embedded AI devices. Study TinyML – the field of applying ML technologies to embed AI in resource-constrained devices. Discover how complex embedded AI applications work on smartphones, drones, and other devices that have constrained processing, memory, power, and other resources. Investigate the signal processing methods and ML models behind important applications that process video, audio, motion, and other signals. Explore how ML frameworks are used to create these applications and use these with embedded AI hardware in hands-on projects. Learn how to choose the right hardware, development tools, and software components for an application. Examine the tradeoffs needed to make decisions about the mix of AI processing that needs to be done on the device and in the cloud. Apply what you have learned and use cutting-edge devices, sensors, signal processing, TinyML methods, and embedded ML frameworks to create an embedded AI device.

Who Should Enroll

This program is ideal for those who wish to learn about the field of embedded AI, acquire the specialized skills needed to create embedded AI solutions, explore new uses, solve problems using constrained edge devices, gain competitive business advantage, and expand career options. The curriculum is designed for embedded systems professionals, software engineers, electrical engineers, computer engineers, computer scientists, data scientists, data engineers, and machine learning scientists.

Career Insight

Occupational summary for computer hardware engineers. Source: Economic Modeling Specialists Intl.

66,270
Annual Job Openings
(2020)
5.3%
Projected Growth
(2020-2029)
$119k
Median Salary
(Highly experienced workers can earn up to $180k)

Program Benefits

  • Use hands-on examples and activities to explore embedded AI applications, technologies, and highly specialized embedded ML software frameworks.
  • Explore the details of embedded ML models (including deep learning neural networks) behind some important applications such as object recognition, wake word processing, and gesture detection.
  • Review the steps required to develop and deploy embedded ML models.
  • Understand the unique challenges posed by the deployment of ML models on resource constrained devices in TinyML applications.
  • Use cutting-edge sensors, devices, signal processing, TinyML methods, and embedded ML frameworks to create an embedded AI device.
  • Collect real-world sensor data, train, and validate ML models, optimize the model for deployment on a resource-constrained device, and deploy the model to your hardware.
  • Explore features of devices that may be used to run embedded AI, including new low power microcontrollers which incorporate neural network accelerators that will enable new battery powered applications to execute complex ML models.
  • Explore current trends and what may be on the horizon for embedded AI.

Course Schedule

Required Courses

TitleWinterSpringSummerFall
EECS X480.1
Introduction to Embedded AI (3.00 Units)
EECS X480.1
to be scheduled      
EECS X480.2
Embedded AI Applications and Technologies
EECS X480.2
       
EECS X480.3
Embedded AI Development
EECS X480.3
       

Course schedules are subject to change. Individual courses may be taken without enrolling in the full program.

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Academic Calendar

Event Fall 2021 Winter 2022 Spring 2022 Summer 2022
Registration Begins (after 2pm PT) Jul 15 Oct 21 Jan 20 Apr 21
Courses Begin* (week of) Sep 20 Jan 3 Mar 28 Jun 20
Courses End* (week of) Dec 13 Mar 14 Jun 6 Sep 6
Parking Permits Expire Dec 31 Mar 31 Jun 30 Sep 30
Administrative Holidays Nov 11, Nov 25-26, Dec 23-24, Dec 30-31 Jan 17, Feb 21 Mar 25, May 30 Jul 4, Sep 5

*Actual dates may vary by program.

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Learn How To Earn Your CertificateSpecialized Studies Award Requirements

A specialized studies certificate is awarded upon completion of 3 courses (9 credit units) with a grade of “C” or better in each course. To receive your digital certificate, submit a Request for Certificate with a non-refundable $35 application fee after completing all program requirements. All requirements must be completed within five (5) years after the student enrolls in his/her first course. Students not pursuing a specialized studies award are welcome to take as many individual courses as they wish.

On-site Training Available

Our Corporate Training specialists can deliver this program or customize one that fits your organization’s specific needs. Visit Corporate Training or call (949) 824-1847 for information.

English Proficiency Requirement
All certificate programs at UCI Division of Continuing Education (classroom and online formats) require professional-level English language proficiency in listening and note-taking, reading comprehension and vocabulary, written expression, and oral presentation.