A new photonic chip achieves image processing at 25 million frames per second with high energy efficiency, promising major advances in real-time imaging and AI applications.
Key Details
- 1Vertically integrated photonic chip ('Gezhi') processes 25 million frames per second.
- 2Uses a mutually incoherent VCSEL array and diffractive neural network (DNN) for computation.
- 3Achieved 98.6% accuracy on MNIST image classification benchmark.
- 4Consumes only 3.52 aJ/μm² of optical energy per frame, outperforming electronic accelerators.
- 5Capable of versatile tasks like edge extraction and denoising.
- 6Can be scaled to larger distributed optical computing with tens of thousands of units.
Why It Matters
This technology could enable real-time, low-power AI implementation for high-throughput imaging tasks, which may dramatically enhance the speed and efficiency of image analysis in radiology and medical imaging fields. Its scalability and superior energy efficiency present opportunities for deploying advanced AI in areas such as autonomous diagnostics and smart healthcare.
Source
EurekAlert
Related News
•EurekAlert
NIH-Backed AI Model Predicts Cancer Survival Using Single-Cell Data
Researchers have developed scSurvival, a machine learning tool that uses single-cell tumor data to accurately predict cancer patient survival and identify high-risk cell populations.
•EurekAlert
Deep Learning Pathomics Platform Improves Immunotherapy Prediction in Lung Cancer
A deep learning pathomics platform accurately predicts immunotherapy response in metastatic NSCLC using routine pathology slides.
•EurekAlert
AI Pathology Model Outperforms PD-L1 in Predicting NSCLC Immunotherapy Response
MD Anderson's Path-IO machine learning platform accurately predicts immunotherapy responses in metastatic non-small cell lung cancer, surpassing current biomarker standards.