Learning to grow machine-learning models
New LiGO technique accelerates training of large machine-learning models, reducing the monetary and environmental cost of developing AI applications.
New LiGO technique accelerates training of large machine-learning models, reducing the monetary and environmental cost of developing AI applications.
The teams will work toward sustainable microchips and topological materials as well as socioresilient materials design.
The chip, which can decipher any encoded signal, could enable lower-cost devices that perform better while requiring less hardware.
Study shows that if autonomous vehicles are widely adopted, hardware efficiency will need to advance rapidly to keep computing-related emissions in check.
New technique significantly reduces training and inference time on extensive datasets to keep pace with fast-moving data in finance, social networks, and fraud detection in cryptocurrency.
New technique could diminish errors that hamper the performance of super-fast analog optical neural networks.
MIT Lincoln Laboratory's new supercomputing facility reduces energy impacts