Year: 2025
Type: article
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.
Source: Science Advances
Authors: Ronald Davis, Zaijun Chen, Ryan Hamerly, Dirk Englund
Language: English
Subfield: Artificial Intelligence
Field: Computer Science
Domain: Physical Sciences
Open Access status: gold
Estimated APC paid: $4,500
OpenAlex ID: W4411193884