Mit Ai Hardware Program News Today

Chandrakasan Named MIT’s Chief Innovation And Strategy Officer.

Artificial intelligence chatbots that can adjust to comprehend a user’s accent or smart keyboards that constantly update to more accurately guess the next word based on a user’s typing history can be made possible by personalized deep-learning models. A machine-learning model must constantly be adjusted with new data to accommodate this personalization.

The PockEngine technique enables deep learning models, like those that underlie AI chatbots or smart keyboards, to efficiently and continuously learn from new user data directly on an edge device like a smartphone.

New computational techniques, “HighLight” and “Tailors and Swiftiles,” could dramatically boost the speed and performance of high-performance computing applications like graph analytics or generative AI. The work, from MIT and NIVIDIA, aims to accelerate sparse tensors for AI models by introducing more efficient and flexible ways to take advantage of sparsity.

New computational techniques, “HighLight” and “Tailors and Swiftiles,” could dramatically boost the speed and performance of high-performance computing applications like graph analytics or generative AI. The work, from MIT and NIVIDIA, aims to accelerate sparse tensors for AI models by introducing more efficient and flexible ways to take advantage of sparsity.

A new technique enables on-device training of machine-learning models on edge devices like microcontrollers, which have very limited memory. This could allow edge devices to continually learn from new data, eliminating data privacy issues, while enabling user customization.