The concept and advantage of reconfigurable technology is introduced. A kind of processor architecture of re configurable macro processor (RMP) model based on FPGA array and DSP is put forward and has been implemented...The concept and advantage of reconfigurable technology is introduced. A kind of processor architecture of re configurable macro processor (RMP) model based on FPGA array and DSP is put forward and has been implemented. Two image algorithms are developed: template-based automatic target recognition and zone labeling. One is estimating for motion direction in the infrared image background, another is line picking-up algorithm based on image zone labeling and phase grouping technique. It is a kind of 'hardware' function that can be called by the DSP in high-level algorithm. It is also a kind of hardware algorithm of the DSP. The results of experiments show the reconfigurable computing technology based on RMP is an ideal accelerating means to deal with the high-speed image processing tasks. High real time performance is obtained in our two applications on RMP.展开更多
Optical computing holds promise for high-speed,energy-efficient information processing,with diffractive optical networks emerging as a flexible platform for implementing task-specific transformations.A challenge,howev...Optical computing holds promise for high-speed,energy-efficient information processing,with diffractive optical networks emerging as a flexible platform for implementing task-specific transformations.A challenge,however,is the effective optimization and alignment of the diffractive layers,which is hindered by the difficulty of accurately modeling physical systems with their inherent hardware imperfections,noise,and misalignments.While existing in situ optimization methods offer the advantage of direct training on the physical system without explicit system modeling,they are often limited by slow convergence and unstable performance due to inefficient use of limited measurement data.Here,we introduce a model-free reinforcement learning approach utilizing Proximal Policy Optimization(PPO)for the in situ training of diffractive optical processors.PPO efficiently reuses in situ measurement data and constrains policy updates to ensure more stable and faster convergence.We validated our method through both simulations and experiments across a range of in situ learning tasks,including targeted energy focusing through a random diffuser,image generation,aberration correction,and optical image classification,demonstrating in each task better convergence and performance.Our strategy operates directly on the physical system and naturally accounts for unknown real-world imperfections,eliminating the need for prior system knowledge or modeling.By enabling faster and more accurate training under realistic experimental constraints,this in situ reinforcement learning approach could offer a scalable framework for various optical and physical systems governed by complex,feedback-driven dynamics.展开更多
With the unstructured grid, the Finite Volume Coastal Ocean Model（FVCOM） is converted from its original FORTRAN code to a Compute Unified Device Architecture（CUDA） C code, and optimized on the Graphic Processor U...With the unstructured grid, the Finite Volume Coastal Ocean Model（FVCOM） is converted from its original FORTRAN code to a Compute Unified Device Architecture（CUDA） C code, and optimized on the Graphic Processor Unit（GPU）. The proposed GPU-FVCOM is tested against analytical solutions for two standard cases in a rectangular basin, a tide induced flow and a wind induced circulation. It is then applied to the Ningbo＇s coastal water area to simulate the tidal motion and analyze the flow field and the vertical tide velocity structure. The simulation results agree with the measured data quite well. The accelerated performance of the proposed 3-D model reaches 30 times of that of a single thread program, and the GPU-FVCOM implemented on a Tesla k20 device is faster than on a workstation with 20 CPU cores, which shows that the GPU-FVCOM is efficient for solving large scale sea area and high resolution engineering problems.展开更多
文摘The concept and advantage of reconfigurable technology is introduced. A kind of processor architecture of re configurable macro processor (RMP) model based on FPGA array and DSP is put forward and has been implemented. Two image algorithms are developed: template-based automatic target recognition and zone labeling. One is estimating for motion direction in the infrared image background, another is line picking-up algorithm based on image zone labeling and phase grouping technique. It is a kind of 'hardware' function that can be called by the DSP in high-level algorithm. It is also a kind of hardware algorithm of the DSP. The results of experiments show the reconfigurable computing technology based on RMP is an ideal accelerating means to deal with the high-speed image processing tasks. High real time performance is obtained in our two applications on RMP.
文摘Optical computing holds promise for high-speed,energy-efficient information processing,with diffractive optical networks emerging as a flexible platform for implementing task-specific transformations.A challenge,however,is the effective optimization and alignment of the diffractive layers,which is hindered by the difficulty of accurately modeling physical systems with their inherent hardware imperfections,noise,and misalignments.While existing in situ optimization methods offer the advantage of direct training on the physical system without explicit system modeling,they are often limited by slow convergence and unstable performance due to inefficient use of limited measurement data.Here,we introduce a model-free reinforcement learning approach utilizing Proximal Policy Optimization(PPO)for the in situ training of diffractive optical processors.PPO efficiently reuses in situ measurement data and constrains policy updates to ensure more stable and faster convergence.We validated our method through both simulations and experiments across a range of in situ learning tasks,including targeted energy focusing through a random diffuser,image generation,aberration correction,and optical image classification,demonstrating in each task better convergence and performance.Our strategy operates directly on the physical system and naturally accounts for unknown real-world imperfections,eliminating the need for prior system knowledge or modeling.By enabling faster and more accurate training under realistic experimental constraints,this in situ reinforcement learning approach could offer a scalable framework for various optical and physical systems governed by complex,feedback-driven dynamics.
基金Project supported by the National Natural Science Foundation of China(Grant No.51279028,51479175)the Public Science and Technology Research Funds Projects of Ocean(Grant No.201405025)
文摘With the unstructured grid, the Finite Volume Coastal Ocean Model（FVCOM） is converted from its original FORTRAN code to a Compute Unified Device Architecture（CUDA） C code, and optimized on the Graphic Processor Unit（GPU）. The proposed GPU-FVCOM is tested against analytical solutions for two standard cases in a rectangular basin, a tide induced flow and a wind induced circulation. It is then applied to the Ningbo＇s coastal water area to simulate the tidal motion and analyze the flow field and the vertical tide velocity structure. The simulation results agree with the measured data quite well. The accelerated performance of the proposed 3-D model reaches 30 times of that of a single thread program, and the GPU-FVCOM implemented on a Tesla k20 device is faster than on a workstation with 20 CPU cores, which shows that the GPU-FVCOM is efficient for solving large scale sea area and high resolution engineering problems.
