Research Laboratory of Electronics
Research Laboratory of Electronics

Robotic probe quickly measures key properties of new materials

Developed to analyze new semiconductors, the system could streamline the development of more powerful solar panels.

Photonic processor could streamline 6G wireless signal processing

By performing deep learning at the speed of light, this chip could give edge devices new capabilities for real-time data analysis.

Collaborating to advance research and innovation on essential chips for AI

Agreement between MIT Microsystems Technology Laboratories and GlobalFoundries aims to deliver power efficiencies for data centers and ultra-low power consumption for intelligent devices at the edge.

Can deep learning transform heart failure prevention?

A deep neural network called CHAIS may soon replace invasive procedures like catheterization as the new gold standard for monitoring heart health.

Photonic processor could enable ultrafast AI computations with extreme energy efficiency

This new device uses light to perform the key operations of a deep neural network on a chip, opening the door to high-speed processors that can learn in real-time.

New security protocol shields data from attackers during cloud-based computation

The technique leverages quantum properties of light to guarantee security while preserving the accuracy of a deep-learning model.

Method prevents an AI model from being overconfident about wrong answers

More efficient than other approaches, the “Thermometer” technique could help someone know when they should trust a large language model.

School of Engineering welcomes new faculty

Fifteen new faculty members join six of the school’s academic departments.

This tiny, tamper-proof ID tag can authenticate almost anything

MIT engineers developed a tag that can reveal with near-perfect accuracy whether an item is real or fake. The key is in the glue on the back of the tag.

New techniques efficiently accelerate sparse tensors for massive AI models

Complimentary approaches — “HighLight” and “Tailors and Swiftiles” — could boost the performance of demanding machine-learning tasks.