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liquid injection pattern

Inspired by nature, temperature-responsive building facades could help reduce energy use from heating and cooling

Large Language Models (LLMs) have high electricity and water consumption due to the resource requirements of serving them to millions of users. This footprint can be reduced using methods developed by Professor Samin Aref (MIE) and his team, which produce smaller LLMs through quantizing their parameters. (image generated by ChatGPT)

How ‘slimmed-down’ large language models can reduce AI’s environmental and energy footprint

Farah Ghizzawi

How a passion for sustainable transportation brought this graduate student from Beirut to Toronto

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New Technique Promises More Efficient Solar Cells, Say U of T Engineering Researchers

March 7, 2013

Women in Science & Engineering Conference a WISE Choice

March 5, 2013

Five U of T Engineers Honoured by the Engineering Institute of Canada

March 4, 2013

Pour, Shake and Stir

February 28, 2013

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