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
AI Method Automates X-ray Absorption Spectroscopy for Material Analysis
Researchers have developed an AI-based approach to automate and enhance the analysis of X-ray absorption spectroscopy (XAS) data for materials science.
•EurekAlert
Zero-Cost EHR-Based AI Tool Scales Dementia Detection in Primary Care
A zero-cost, EHR-integrated AI tool significantly boosts dementia detection in primary care clinics without adding clinician workload.
•EurekAlert
BraDiPho: New 3D AI Atlas Integrates Brain Dissections with MRI
Researchers have developed BraDiPho, a tool that merges ex-vivo photogrammetric brain dissection data with in-vivo MRI tractography using AI.