Online Specialized Studies Program in Embedded AI

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 edges 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.

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.

Earn an Alternative Digital Credential

Successful completion of a qualifying assignment within the Introduction to Embedded AI course offers you the opportunity to earn an Alternative Digital Credential (ADC). Also referred to as a digital badge, an ADC is a virtual record of specific skills and competencies you have acquired and provides a verifiable way for you to share your educational achievements with employers, colleagues, and others via social platforms like LinkedIn, Facebook, and Twitter.

Specialized 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.