Researchers at the University of Sydney have developed an ultra-compact AI chip using light for rapid and energy-efficient image computation, successfully classifying medical images including MRI scans.
Key Details
- 1Nano photonic AI chip performs neural computations using light, not electricity.
- 2Prototype was validated by classifying over 10,000 biomedical images, including MRI scans of breast, chest, and abdomen.
- 3Achieved classification accuracy of approximately 90-99% in experiments and simulations.
- 4Chip performs calculations on a picosecond timescale (trillionths of a second).
- 5The technology aims to enable faster, more energy-efficient AI processing with a minimal energy footprint.
- 6A patent has been filed and further work is planned to scale the technology.
Why It Matters
This innovation in AI hardware could drastically reduce power consumption and increase the speed of medical imaging AI, offering a sustainable solution as computational demands grow in clinical radiology. It may enable real-time analysis and more scalable deployment of imaging AI systems.
Source
EurekAlert
Related News
•EurekAlert
UVA's $4M NIH Grant Backs AI to Optimize Cardiac MRI Therapies
UVA secures $4 million NIH funding to develop GenStrain, an AI model using MRI data to personalize cardiac pacing therapies.
•EurekAlert
Machine Learning Models Predict Cancer Risk with Routine Health Data
Researchers developed machine learning models using routinely collected health data to predict individual risk for 20 common cancers, outperforming traditional methods for several cancer types.
•EurekAlert
Duke Receives $25M to Lead National Trustworthy AI Hub for Dementia and Aging
Duke University has launched a $25 million national center to make AI for aging and dementia clinically robust and trustworthy.