Tiny machine learning is broadly defined as a fast growing field of machine learning technologies and applications including hardware, algorithms and software capable of performing on-device sensor data analytics at extremely low power, typically in the mW range and below, and hence enabling a variety of always-on use-cases and targeting battery operated devices.
The tinyML Summit 2022 Technical Program Committee invites contributions from experts from industry, academia, start-ups and government labs to submit abstracts for posters in the areas of tinyML for audio, vision, sensors, hardware and software and tools.
We are very excited to announce the winners of Eyes on Edge: tinyML Vision Challenge! First we would like to thank the 485 people/teams that participated in our inaugural challenge we held with Hackster.io.
As device sensors proliferate across every company’s value chain – from new product development through inspection, tracking, and delivery – tinyML is surfacing to provide actionable insights, transforming business as we know it. There are sound economic reasons for all this interest and activity. McKinsey researchers predict IoT will have a potential economic impact of US $4-11 trillion by 2025, identifying manufacturing as the largest vertical (US $1.2-3.7 trillion).
While machine-learning (ML) development activity most visibly focuses on high-power solutions in the cloud or medium-powered solutions at the edge, there is another collection of activity aimed at implementing machine learning on severely resource-constrained systems.
Known as TinyML, it’s both a concept and an organization — and it has acquired significant momentum over the last year or two.
“TinyML deployments are powering a huge growth in ML deployment,…