The explosive volume growth of deep-learning(DL)applications has triggered an era in computing,with neuromorphic photonic platforms promising to merge ultra-high speed and energy efficiency credentials with the brain-...The explosive volume growth of deep-learning(DL)applications has triggered an era in computing,with neuromorphic photonic platforms promising to merge ultra-high speed and energy efficiency credentials with the brain-inspired computing primitives.The transfer of deep neural networks(DNNs)onto silicon photonic(SiPho)architectures requires,however,an analog computing engine that can perform tiled matrix multiplication(TMM)at line rate to support DL applications with a large number of trainable parameters,similar to the approach followed by state-of-the-art electronic graphics processing units.Herein,we demonstrate an analog SiPho computing engine that relies on a coherent architecture and can perform optical TMM at the record-high speed of 50 GHz.Its potential to support DL applications,where the number of trainable parameters exceeds the available hardware dimensions,is highlighted through a photonic DNN that can reliably detect distributed denial-of-service attacks within a data center with a Cohen’s kappa score-based accuracy of 0.636.展开更多
基金the EU-projects PlasmoniAC(Grant No.871391)SIPHO-G(Grant No.101017194)Hellenic Foundation for Research and Innovation(H.F.R.I.)under the“First Call for H.F.R.I.Research Projects to Support Faculty Members and Researchers and the Procurement of High-cost Research Equipment Grant”(Grant No.4233,DeepLight).
文摘The explosive volume growth of deep-learning(DL)applications has triggered an era in computing,with neuromorphic photonic platforms promising to merge ultra-high speed and energy efficiency credentials with the brain-inspired computing primitives.The transfer of deep neural networks(DNNs)onto silicon photonic(SiPho)architectures requires,however,an analog computing engine that can perform tiled matrix multiplication(TMM)at line rate to support DL applications with a large number of trainable parameters,similar to the approach followed by state-of-the-art electronic graphics processing units.Herein,we demonstrate an analog SiPho computing engine that relies on a coherent architecture and can perform optical TMM at the record-high speed of 50 GHz.Its potential to support DL applications,where the number of trainable parameters exceeds the available hardware dimensions,is highlighted through a photonic DNN that can reliably detect distributed denial-of-service attacks within a data center with a Cohen’s kappa score-based accuracy of 0.636.
