Soochow University Lifts Photonic AI to 100 Million Images a Second
Updated
Updated · EIN Presswire · Jul 24
Soochow University Lifts Photonic AI to 100 Million Images a Second
1 articles · Updated · EIN Presswire · Jul 24
Summary
A Soochow University team reported a compression-decompression framework that pushes photonic neuromorphic computing to 100 million images per second and 600 million data points per second.
The method compresses inputs into a task-relevant low-dimensional space using eigenvector analysis, then decompresses them to recover information that would normally be lost in faster, smaller models.
Tests on 2 platforms—a continuous-wave laser system and a fabricated photonic neuro-synaptic chip—showed latency and hardware demands fell by an order of magnitude while accuracy stayed comparable to uncompressed architectures.
The researchers say the approach breaks the usual speed-accuracy trade-off in photonic neuromorphic systems, offering a more scalable path for low-power, high-throughput AI hardware beyond conventional von Neumann limits.