Climate Implications of Computing & Communications
Thursday, March 3 & Friday, March 4, 2022 | 10:00am - 2:00pm ET
Multiple Speakers
How MIT’s Muriel Medard Pioneered the Universal Decoder
Kathy Pretz | IEEE Spectrum
Researchers developed a new algorithm and created an efficient silicon chip that eliminates the need for custom decoding hardware to spot signal errors.
What’s Next in AI | Enabling the Future of Computing through Efficient AI
Tuesday, December 7, 2021 | 10:00am-10:30am ET
Speaker: Dr. David Cox, MIT-IBM Watson AI Lab
From Smaller Transistors to Cleaner Qubits: The Continuous Quest for Higher Performance in Computing
Wednesday, October 20, 2021 | 12:00pm ET
Speaker: Iuliana Radu, IMEC
Natural Language Processing Accelerator for Transformer Models
Song Han, Anantha Chandrakasan
This project aims to develop efficient processors for natural language processing directly on an edge device to ensure privacy, low latency and extended battery life. The goal is to accelerate the entire transformer model (as opposed to just the attention mechanism) to reduce data movement across layers.
Accelerating AI at the Speed of Light
Daniel de Wolff | MIT Startup Exchange
Yichen Shen PhD '16 is CEO of Lightelligence, an MIT spinout using photonics to reinvent computing for artificial intelligence.
Toward Brain-inspired, Energy-efficient Chips
Monday, March 26, 2021 | 12pm - 1pm EST
Panel Discussion: Bilge Yildiz, Michale Fee, Jesus del Alamo, Ju Li, Aude Oliva
AI Algorithms Are Slimming Down to Fit in Your Fridge
Will Knight | Wired Magazine
Artificial intelligence programs typically are power guzzlers. New research shows how to generate computer vision from a simple, low-power chip.
Faster Video Recognition for the Smartphone Era
Kim Martineau | MIT Quest for Intelligence
A new technique for training video recognition models is up to three times faster than current state-of-the-art methods while improving runtime performance on mobile devices.
Powerful Computer Vision Algorithms are Now Small Enough to Run on Your Phone
Karen Hao | MIT Technology Review
Researchers have shrunk state-of-the-art computer vision models to run on low-power devices.