tinyML Talks by Blair Newman and Roman Rusak from Neuton.AI and Tamas Daranyi

Announcing tinyML Talks on October 10th, 2023

IMPORTANT: Please register here
[https://us02web.zoom.us/webinar/register/7916922063082/WN_8uFdOcMpT3ejRssvLN57og](https://us02web.zoom.us/webinar/register/7916922063082/WN_8uFdOcMpT3ejRssvLN57og)

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)
Blair Newman, CTO, Neuton.AI
Roman Rusak, Principal Embedded Software Engineer, Neuton.AI
Tamas Daranyi, Platform Product Management, Silicon Labs
“Buttonless Remote Control – Reproduce on your device”

Edge Devices or Always-on devices often possess limited functionality, memory, and battery life that do not meet business requirements. This forces developers to find a balance between data analysis, functionality, complexity, energy capacity, and consumption. At Neuton.AI, we have developed an innovative approach to help people create compact neural networks that can recognize complex activities with minimal memory and energy consumption. In this tutorial, you will learn how to enhance the ability of users to control consumer electronics devices with just hand gestures and how to create a similar TinyML solution of only 4Kb in total footprint by yourself.
With a universal gesture-based remote control, you can easily access and control any Bluetooth-enabled media system or presentation slides without physical contact. Neuton’s Gesture Recognition Model leveraging Silicon Labs xG24 Dev Kit for EFR32MG24 Wireless SoC can recognize eight different types of gestures with almost 99% accuracy, including swipe right, swipe left, double tap, double knock, clockwise rotation, counterclockwise rotation, idle, and an unknown class. Moreover, the Inference time for this model is less than 2.3 ms, and it has a memory footprint of only 4.2 KB in Flash and 1.4 KB RAM consumption.
This webinar will include:
– Deep dive into the solution creation process
– Different TinyML use cases’ overview
– Silicon Labs product line presentation

With strong tech expertise and 20+ years of leadership experience, Blair Newman provides unprecedented insights into the future of AI’s development and use. As Neuton’s CTO, Blair is engaged in overseeing our business solutions as well as ensuring high-quality services are delivered to our clients. Prior to Neuton, Blair held various leadership roles at T-Systems North America, a Division of Deutsche Telekom, being responsible for providing strategic direction and leadership in the areas of Dynamic Services (Cloud Computing), SAP Hosting, Application Operations, Managed Hosting and Infrastructure Services.

Roman Rusak is a Principal Embedded Software Engineer from Neuton.AI. During his 7+ years in embedded development he has delivered many innovative AIoT and Wearable products to the market, such as Sport Trackers, HVAC monitoring systems, Logistics Trackers, etc. Working as an application and algorithm engineer, he is helping customers with creation and optimization of advanced TinyML solutions for their products from different market segments. Now he is focused on algorithms for the Smart Sensor and Ultra-TinyML solutions with Neuton.AI.

Tamas Daranyi, Product Manager, responsible for driving AI/ML initiatives at Silicon Labs. He holds an MSc, Electrical Engineering. He has working experience in various IoT focused projects in the past 10+ years ranging from the IoT end-node up to cloud service product development. Prior to Silicon Labs he had engineering and product management roles at GE, LogMeIn (Xively IoT) and Google Cloud IoT, CloudAI.

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: [https://www.youtube.com/tinyML?sub_confirmation=1](https://www.youtube.com/tinyML?sub_confirmation=1)

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