Article #5 of Confronting AI Series: Smart ML on embedded systems demands efficiency under tight memory, compute, and latency constraints. Techniques like pruning, quantization, low-rank decomposition, and federated learning enable compact, fast, and reliable models for edge intelligence.
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ORGANIZATIONS. SHAPING THE INDUSTRY.
Knowles Corporation
Electrical and Electronic Manufacturing
As a market leader and global supplier of advanced micro-acoustic microphon...
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Pose detection technology leverages advanced machine learning algorithms to interpret human movements in real-time, enabling seamless, intuitive device control through simple gestures.
Nvidia Jetson and Edge Impulse facilitate Edge AI development, offering computational power and model training platforms for real-world applications.
Audio event detection functions can listen for distinct sounds or patterns embedded within an audio stream. It uses advanced signal processing and machine learning algorithms to accurately identify and classify sound events in real-time, with no internet connectivity required.
Sensor fusion enables the seamless integration of data from multiple sensors, paving the way for advanced Edge AI implementations that optimize real-time processing, enhance decision-making, and boost system responsiveness in dynamic environments.
Article #5 of Spotlight on Innovations in Edge Computing and Machine Learning: A computer vision project that monitors vehicle traffic in real-time using video inferencing performed on the Brainchip Akida Development Kit.
Article #2 of Spotlight on Innovations in Edge Computing and Machine Learning: Edge AI techniques such as Keyword Spotting can turn an ordinary device into a smart appliance.
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LwM2M may be the missing link to implement continual learning in TinyML projects.
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ABI Research forecasts that TinyML unit volumes will explode from 15 million units in 2020 to 2.5 billion units in 2030. Find out about this machine-learning technique now.
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Designed to bring machine learning to the Earth's last great frontier, a prototype smart sensor platform users on-device inference to classify marine mammal calls — powering itself exclusively from the sounds it captures.
The CyberSpec framework is designed to detect anomalous behavior linked to cyber-attacks against crowd-sensing spectrum sensors, even when said sensors are running on lightweight resource-constrained hardware like a Raspberry Pi.
In this article, we look at two tinyML projects for education. We show how Backyard Brains uses low-cost experiment kits to make neuroscience education more accessible. We also introduce our readers to a specialisation offered by Harvard & Google to help students learn tinyML like never before.
A holistic open-source framework to improve the performance of multiple modalities and outperform the existing single-modality algorithms.
In this article, we take a look at two tinyML projects related to healthcare. The first project helps gather Mean Radiant Temperature data outdoors to protect people from extreme heat, and the other one is a solution for affordable, accurate, & rapid detection of pneumonia.
In this article, we take a look at two tinyML projects that have the potential to make contributions towards sustainable development goals. While the first project is about revolutionising precision farming, the second one aims to create a network of low-cost sensors for mapping carbon emissions.
