December 2, 2024
The deep neural network models that power today’s most demanding machine-learning applications have grown so large and complex that they are pushing the limits of traditional electronic computing hardware.
Photonic hardware, which can perform machine-learning computations with light, offers a faster and more energy-efficient alternative. However, there are some types of neural network computations that a photonic device can’t perform, requiring the use of off-chip electronics or other techniques that hamper speed and efficiency.
Complete article from MIT News.
Explore
What Makes a Good Proton Conductor?
Zach Winn | MIT News
MIT researchers found a way to predict how efficiently materials can transport protons in clean energy devices and other advanced technologies.
New Tool Makes Generative AI Models More Likely to Create Breakthrough Materials
Zach Winn | MIT News
With SCIGEN, researchers can steer AI models to create materials with exotic properties for applications like quantum computing.
New Transmitter could Make Wireless Devices more Energy-Efficient
Adam Zewe | MIT News
The flexible chip could boost the performance of current electronics and meet the more stringent efficiency requirements of future 6G technologies.




