2025-06-11 マサチューセッツ工科大学(MIT)
This image shows an artist’s interpretation of new optical processor for an edge device, developed by MIT researchers, that performs machine learning computations at the speed of light, classifying wireless signals in a matter of nanoseconds.
Credits:Credit: Sampson Wilcox, Research Laboratory of Electronics
＜関連情報＞
- https://news.mit.edu/2025/photonic-processor-could-streamline-6g-wireless-signal-processing-0611
- https://www.science.org/doi/10.1126/sciadv.adt3558
シャノン制限されたデータ移動を持つRFフォトニック深層学習プロセッサー RF-photonic deep learning processor with Shannon-limited data movement
Ronald Davis III, Zaijun Chen, Ryan Hamerly, and Dirk Englund
Science Advances Published:11 Jun 2025
DOI:https://doi.org/10.1126/sciadv.adt3558
Abstract
Edholm’s law predicts exponential growth in data rate and spectrum bandwidth for communications. Owing to exponentially increasing deep neural network computing demands and the slowing of Moore’s law, new computing paradigms are required for future advanced communications like 6G. Optical neural networks (ONNs) are promising accelerators but struggle with scalability and system overhead. Here, we introduce our multiplicative analog frequency transform optical neural network (MAFT-ONN), an artificial intelligence hardware accelerator that experimentally computes fully analog deep learning on raw radio frequency (RF) signals, performing modulation classification that quickly converges to 95% accuracy. MAFT-ONN also exhibits scalability with nearly 4 million fully analog operations for MNIST digit classification. Because of the Shannon capacity–limited analog data movement, MAFT-ONN is also hundreds of times faster than traditional RF receivers.
