tinyML: Enabling low Power ML at the Edge-Silicon Valley, CA

Announcing tinyML Talks on May 7th, 2024
IMPORTANT: Please register here

Once registered, you will receive a link and dial in information to teleconference by email, that you can also add to your calendar.

8:00 AM - 9:00 AM Pacific Daylight Time (PDT)
Varun Chari, Professor, Staff Software Engineer, Arm
"Streamlining tinyML application development using open-CMSIS and visual studio code"

Arm released the Arm CMSIS csolution extension in Visual Studio Code (VSCode), a widely used code editor and IDE in the software community. This extension, along with other Arm extensions, provides a seamless developer experience. This talk aims to provide more insights into developing an ML application using these extensions and open-CMSIS packs and deploying it on software emulated and hardware platforms.

Varun is a Staff Software Engineer in Strategic Alliances Technical Marketing Team at Arm. He focuses on enabling and leading software strategies on emerging technologies relevant to Arm across the strategic partners (Google, Meta, Amazon, Microsoft) in Machine Learning and IoT space.

We encourage you to register earlier since on-line broadcast capacity may be limited.

Note: tinyML Talks slides and videos will be available on the tinyML website and tinyML YouTube Channel afterwards, for those who missed the live session. Please take a moment and subscribe to the YouTube channel today

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IMPORTANT: Please register here

Once registered, you will receive a link and dial in information to teleconference by email, that you can also add to your calendar.

8:00 AM - 9:00 AM Pacific Daylight Time (PDT)
Zhaojing (Jim) Huang, PhD Candidate at the School of Biomedical Engineering, University of Sydney
"Unleashing The Power of Tiny Neural Network Models in Medical Devices"

In today's rapidly evolving healthcare landscape, the integration of machine learning technology is driving fundamental transformations. Picture a future where wearable devices become indispensable allies, offering unprecedented precision in diagnosing medical conditions. This presentation delves deep into the realm of advanced and efficient neural network models, particularly tinyML, and their pivotal role in reshaping the future of medical diagnosis through long-term monitoring. Furthermore, personalization emerges as a crucial need that impacts the need and use around the medical devices design and technology. Continuously evolving and innovative models are refining on-edge performances, thereby enhancing the potential of the next generation of these devices. During this session, we will explore some of the novel and compatible tinyML models that have shown promising potential, discussing their implications and contributions to the future.

Zhaojing (Jim) Huang is a second-year PhD student in the School of Biomedical Engineering at the University of Sydney. His research focuses on the application of tinyML in the analysis of bio-signal data, particularly in the realm of medical diagnostics. With a keen interest in leveraging cutting-edge technology for healthcare advancements, he is committed to exploring the potential of machine learning in addressing critical challenges in biomedical engineering.

We encourage you to register earlier since on-line broadcast capacity may be limited.

Note: tinyML Talks slides and videos will be available on the tinyML website and tinyML YouTube Channel afterwards, for those who missed the live session. Please take a moment and subscribe to the YouTube channel today

#@#