IEEE CIS San Diego Talk: TinyML - Bringing Artificial Intelligence to Tiny Devices
IEEE CIS San Diego Talk: TinyML: Bringing Artificial Intelligence to Tiny Devices
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Abstract: Artificial Intelligence (AI) is no longer limited to powerful computers and cloud servers. What if everyday devices could sense, learn, and make intelligent decisions without relying on the internet?
This session introduces Tiny Machine Learning (TinyML), an emerging field that enables machine learning models to run on resource-constrained devices such as microcontrollers and embedded systems. Through real-world examples, including smart wearables, voice recognition, and predictive maintenance, students will explore how TinyML bridges the gap between AI and embedded hardware. The session will cover TinyML fundamentals, its differences from cloud-based AI, the hardware and software ecosystem, and the process of deploying machine learning models onto tiny devices. Students will also gain insights into practical applications, engineering challenges, and opportunities to build their own intelligent edge devices.
Target Audience: Undergraduate Engineering Students
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Speaker Bio — Pragathi Prema Kumar
Pragathi Prema Kumar is a Senior Embedded Software Engineer at Qualcomm Innovation Center, with over seven years of experience in embedded systems, firmware development, and platform-level software. Her expertise includes low-level embedded software development, system debugging, and hardware-software integration.
She holds a Master’s degree in Electrical Engineering from the University of Texas at Arlington and has completed a Post Graduate Program in Artificial Intelligence and Machine Learning from the University of Texas at Austin.
An IEEE Senior Member, Pragathi serves as Vice Chair of the IEEE Computational Intelligence Society (CIS), IEEE Computer Society, and IEEE Women in Engineering (WIE) in San Diego. She actively contributes to the engineering community through technical talks, research paper reviews, panel discussions, mentoring, and STEM outreach initiatives.
Passionate about bridging embedded systems and artificial intelligence, Pragathi enjoys sharing industry insights and inspiring the next generation of engineers to explore emerging technologies such as TinyML, Edge AI, and intelligent embedded systems.
Date and Time
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- 5998 Alcalá Park Way
- San Diego, California
- United States 92110
- Building: BEC
- Room Number: 301
- Starts 26 October 2026 07:00 PM UTC
- Ends 30 October 2026 07:00 AM UTC
- No Admission Charge
Speakers
Pragathi Prema Kumar of Qualcomm
Title: TinyML: Bringing Artificial Intelligence to Tiny Devices
Artificial Intelligence (AI) is no longer limited to powerful computers and cloud servers. What if everyday devices could sense, learn, and make intelligent decisions without relying on the internet?
This session introduces Tiny Machine Learning (TinyML), an emerging field that enables machine learning models to run on resource-constrained devices such as microcontrollers and embedded systems.
Through real-world examples, including smart wearables, voice recognition, and predictive maintenance, students will explore how TinyML bridges the gap between AI and embedded hardware. The session will cover TinyML fundamentals, its differences from cloud-based AI, the hardware and software ecosystem, and the process of deploying machine learning models onto tiny devices.
Students will also gain insights into practical applications, engineering challenges, and opportunities to build their own intelligent edge devices.
Biography:
Pragathi Prema Kumar is a Senior Embedded Software Engineer at Qualcomm Innovation Center, with over seven years of experience in embedded systems, firmware development, and platform-level software. Her expertise includes low-level embedded software development, system debugging, and hardware-software integration.
She holds a Master’s degree in Electrical Engineering from the University of Texas at Arlington and has completed a Post Graduate Program in Artificial Intelligence and Machine Learning from the University of Texas at Austin.
An IEEE Senior Member, Pragathi serves as Vice Chair of the IEEE Computational Intelligence Society (CIS), IEEE Computer Society, and IEEE Women in Engineering (WIE) in San Diego. She actively contributes to the engineering community through technical talks, research paper reviews, panel discussions, mentoring, and STEM outreach initiatives.
Passionate about bridging embedded systems and artificial intelligence, Pragathi enjoys sharing industry insights and inspiring the next generation of engineers to explore emerging technologies such as TinyML, Edge AI, and intelligent embedded systems.