Unlocking TinyML: A Cost-Efficient Approach to Custom Microcontroller Design for AI Applications Mohamed Shalan Head of Design and EDA Efabless
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Buttonless Remote Control – Reproduce on your device” Blair Newman CTO Neuton.AI Danil Zherebtsov Head of Machine Learning & Analytics
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Minimizing resource usage in microcontrollers for cost effective solutions Ilya Gozman Senior Fellow, Chief AI Architect Grovety The spotlight will
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“Suitability of Forward-Forward and PEPITA Learning to MLCommons-Tiny benchmarks” Danilo Pau Technical Director, IEEE, AAIA & ST Fellow System Research
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“Unsupervised Federated Learning” Ranjitha Prasad Assistant Professor IIIT Delhi Modern day applications such as autonomous vehicles, IoT, smart grids, etc.,
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“Enabling on-device learning on STM32 microcontrollers” Beatrice Rossi Research Scientist STMicroelectronics Michele Craighero PhD Student Politecnico di Milano Most of
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A hardware-aware neural architecture search algorithm targeting ultra-low-power microcontrollers Andrea Mattia Garavagno PhD student Sant’Anna School of Advanced Studies of
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“Biosensing at the edge of the Cloud” Martin Peacock Director and CSO Zimmer and Peacock One of the last frontiers
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“Mudra: Reinventing Tiny Interfaces for Ubiquitous Digital Interaction” Leeor Langer Co-founder and CTO Wearable Devices Oleksandr Pirotskiy Team Leader of
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Empowering the Edge: Advancements in AI Hardware and In-Memory Computing Architectures for TinyML Nitin Chawla Fellow and Director ST Microelectronics
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