RF-photonic deep learning processor with Shannon-limited data movement.
basic_science · Level V
Where this comes from
- Record sourced from PubMed, PMID 40498817.
- Also identified by DOI 10.1126/sciadv.adt3558 and PMC identifier 12153973.
- Licence recorded as CC BY.
- The licence permits redistribution, so the abstract is shown in full and the full text is available from the publisher.
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.