Tiny machine learning design alleviates a bottleneck in memory usage on internet-of-things devices (Lauren Hinkel, MIT News)

December 14, 2021

Machine learning provides powerful tools to researchers to identify and predict patterns and behaviors, as well as learn, optimize, and perform tasks. This ranges from applications like vision systems on autonomous vehicles or social robots to smart thermostats to wearable and mobile devices like smartwatches and apps that can monitor health changes. While these algorithms and their architectures are becoming more powerful and efficient, they typically require tremendous amounts of memory, computation, and data to train and make inferences.

Tiny machine learning design alleviates a bottleneck in memory usage on internet-of-things devices | MIT News | Massachusetts Institute of Technology

Marco Emanuele

Marco Emanuele è appassionato di cultura della complessità, cultura della tecnologia e relazioni internazionali. Approfondisce il pensiero di Hannah Arendt, Edgar Morin, Raimon Panikkar. Marco ha insegnato Evoluzione della Democrazia e Totalitarismi, è l’editor di The Global Eye e scrive per The Science of Where Magazine. Marco Emanuele is passionate about complexity culture, technology culture and international relations. He delves into the thought of Hannah Arendt, Edgar Morin, Raimon Panikkar. He has taught Evolution of Democracy and Totalitarianisms. Marco is editor of The Global Eye and writes for The Science of Where Magazine.

Tiny machine learning design alleviates a bottleneck in memory usage on internet-of-things devices (Lauren Hinkel, MIT News)

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