The exponential growth in data traffic has driven significant research into maximizing the capacity of free-space optical(FSO)communication systems.Orbital angular momentum(OAM)multiplexing offers a promising approach...The exponential growth in data traffic has driven significant research into maximizing the capacity of free-space optical(FSO)communication systems.Orbital angular momentum(OAM)multiplexing offers a promising approach by using spatially structured beams with helical wavefronts to achieve higher data transmission rates.However,conventional electronic convolutional-neural-network-based OAM demultiplexing schemes exhibit substantial computational and energy efficiency limitations.展开更多
Convolutional operations are computationally intensive in artificial intelligence(AI)services,and their overhead in electronic hardware limits machine learning scaling.Here,we introduce a photonic joint transform corr...Convolutional operations are computationally intensive in artificial intelligence(AI)services,and their overhead in electronic hardware limits machine learning scaling.Here,we introduce a photonic joint transform correlator(pJTC)using a near-energy-free on-chip Fourier transformation to accelerate convolution operations.The pJTC reduces computational complexity for both convolution and cross-correlation from O(N^(4))to O(N^(2)),where N^(2)is the input data size.Demonstrating functional Fourier transforms and convolution,this pJTC achieves 98.0%accuracy on an exemplary Modified National Institute of Standards and Technology inference task.Furthermore,a wavelength-multiplexed pJTC architecture shows potential for high throughput and energy efficiency,reaching 305 TOPS/W and 40.2 TOPS/mm^(2),based on currently available foundry processes.An efficient,compact,and low-latency convolution accelerator promises to advance next-generation AI capabilities across edge demands,high-performance computing,and cloud services.展开更多
Photonic Random-Access Memories(P-RAM)are an essential component for the on-chip non-von Neumann photonic computing by eliminating optoelectronic conversion losses in data links.Emerging Phase-Change Materials(PCMs)ha...Photonic Random-Access Memories(P-RAM)are an essential component for the on-chip non-von Neumann photonic computing by eliminating optoelectronic conversion losses in data links.Emerging Phase-Change Materials(PCMs)have been showed multilevel memory capability,but demonstrations still yield relatively high optical loss and require cumbersome WRITE-ERASE approaches increasing power consumption and system package challenges.Here we demonstrate a multistate electrically programmed low-loss nonvolatile photonic memory based on a broadband transparent phase-change material(Ge2Sb2Se5,GSSe)with ultralow absorption in the amorphous state.A zero-staticpower and electrically programmed multi-bit P-RAM is demonstrated on a silicon-on-insulator platform,featuring efficient amplitude modulation up to 0.2 dB/μm and an ultralow insertion loss of total 0.12 dB for a 4-bit memory showing a 100×improved signal to loss ratio compared to other phase-change-materials based photonic memories.We further optimize the positioning of dual microheaters validating performance tradeoffs.Experimentally we demonstrate a half-a-million cyclability test showcasing the robust approach of this material and device.Low-loss photonic retention-of-state adds a key feature for photonic functional and programmable circuits impacting many applications including neural networks,LiDAR,and sensors for example.展开更多
文摘The exponential growth in data traffic has driven significant research into maximizing the capacity of free-space optical(FSO)communication systems.Orbital angular momentum(OAM)multiplexing offers a promising approach by using spatially structured beams with helical wavefronts to achieve higher data transmission rates.However,conventional electronic convolutional-neural-network-based OAM demultiplexing schemes exhibit substantial computational and energy efficiency limitations.
基金supported by the Office of Naval Research(Award Nos.N00014-19-1-2595 and N00014-23-1-2687).Russell L.T.Schwartz is partially supported by the L3Harris PhD student fellowship.
文摘Convolutional operations are computationally intensive in artificial intelligence(AI)services,and their overhead in electronic hardware limits machine learning scaling.Here,we introduce a photonic joint transform correlator(pJTC)using a near-energy-free on-chip Fourier transformation to accelerate convolution operations.The pJTC reduces computational complexity for both convolution and cross-correlation from O(N^(4))to O(N^(2)),where N^(2)is the input data size.Demonstrating functional Fourier transforms and convolution,this pJTC achieves 98.0%accuracy on an exemplary Modified National Institute of Standards and Technology inference task.Furthermore,a wavelength-multiplexed pJTC architecture shows potential for high throughput and energy efficiency,reaching 305 TOPS/W and 40.2 TOPS/mm^(2),based on currently available foundry processes.An efficient,compact,and low-latency convolution accelerator promises to advance next-generation AI capabilities across edge demands,high-performance computing,and cloud services.
基金This work was performed in part at the George Washington University Nanofabrication and Imaging Center(GWNIC).Thin film material analysis is supported by NIST Center for Nanoscale Science and Nanotechnology(CNST),and J.A.Woollam Co.V.J.S.is supported by AFOSR(FA9550-20-1-0193)under the Presidential Early Career Award in Science and Engineering(PECASE).
文摘Photonic Random-Access Memories(P-RAM)are an essential component for the on-chip non-von Neumann photonic computing by eliminating optoelectronic conversion losses in data links.Emerging Phase-Change Materials(PCMs)have been showed multilevel memory capability,but demonstrations still yield relatively high optical loss and require cumbersome WRITE-ERASE approaches increasing power consumption and system package challenges.Here we demonstrate a multistate electrically programmed low-loss nonvolatile photonic memory based on a broadband transparent phase-change material(Ge2Sb2Se5,GSSe)with ultralow absorption in the amorphous state.A zero-staticpower and electrically programmed multi-bit P-RAM is demonstrated on a silicon-on-insulator platform,featuring efficient amplitude modulation up to 0.2 dB/μm and an ultralow insertion loss of total 0.12 dB for a 4-bit memory showing a 100×improved signal to loss ratio compared to other phase-change-materials based photonic memories.We further optimize the positioning of dual microheaters validating performance tradeoffs.Experimentally we demonstrate a half-a-million cyclability test showcasing the robust approach of this material and device.Low-loss photonic retention-of-state adds a key feature for photonic functional and programmable circuits impacting many applications including neural networks,LiDAR,and sensors for example.
