February 24, 2021
IBM Analog Hardware Acceleration Kit: A Flexible and Fast PyTorch Toolkit for Simulating ANN Training and Inference on Resistive Crossbar Arrays
Wednesday, February 24, 2021 Speaker: Malte Rasch, IBM
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December 13, 2020
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.
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December 1, 2020
Shrinking Massive Neural Networks Used to Model Language
Daniel Ackerman | MIT News Office Researcher Jonathan Frankle and his “lottery ticket hypothesis” posits that, hidden within massive neural networks, leaner subnetworks can…
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November 13, 2020
System Brings Deep Learning to “Internet of Things” Devices
Daniel Ackerman | MIT News Office Deep learning is everywhere. This branch of artificial intelligence curates your social media and serves your Google search results.
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August 7, 2020
Shrinking Deep Learning’s Carbon Footprint
Kim Martineau | MIT Quest for Intelligence In June 2020, OpenAI unveiled the largest language model in the world, a text-generating tool called GPT-3 that can write creative…
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June 8, 2020
Engineers Put Tens of Thousands of Artificial Brain Synapses on a Single Chip
Jennifer Chu | MIT News Office MIT engineers have designed a “brain-on-a-chip,” smaller than a piece of confetti, that is made from tens of thousands of artificial brain synapses…
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April 30, 2020
A Foolproof Way to Shrink Deep Learning Models
Kim Martineau | MIT Quest for Intelligence MIT researchers have proposed a technique for shrinking deep learning models that they say is simpler and produces more accurate results…
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October 11, 2019
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…
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October 11, 2019
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.
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June 5, 2019
Chip Design Drastically Reduces Energy Needed to Compute with Light
Rob Matheson | MIT News Office Simulations suggest photonic chip could run optical neural networks 10 million times more efficiently than its electrical counterparts.
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