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 Asia Technical Forum 2023 will continue the tradition of high-quality state-of-the-art presentations. Find out more about sponsoring and supporting the tinyML Foundation.
We are happy to congratulate these companies on earning Awards for their innovative tinyML products and solutions in the following categories:
The tinyML Foundation Deployment Working Group is pleased to publish our first white paper exploring the challenges and solutions for deploying ultra-low power machine learning (ML) at the end of the cloud!
In a bid to address the rising concern of traffic-related fatalities and injuries, the Global tinyML Traffic Hackathon is set to take place in partnership with the City of San José’s Vision Zero program. With pedestrian fatalities constituting a significant portion of traffic-related deaths, the hackathon aims to leverage the power of energy efficient Machine Learning (tinyML) to detect pedestrians and create innovative solutions for enhancing traffic safety. This article delves into the key details of the hackathon, the technology engineer’s will be utilizing and its potential impact on traffic safety.
The 3rd Annual tinyML EMEA Innovation Forum, which took place in Amsterdam from June 26-28, recently wrapped up. One of the objectives of the event was to unite the tinyML EMEA Community to empower and accelerate Innovation and Partnerships. The event drew a diverse range of participants, speakers, and sponsors, all converging to engage in insightful discussions and share ideas about the most recent developments and promising future of energy-efficient machine learning at the edge – tinyML.
From sleep monitoring wearables to face detection models, tinyML is making a big impact on the way we can gather and apply data. As tinyML technologies and ecosystems are gaining more momentum and maturity, more and more applications are being developed and deployed in different verticals.