Extreme Ultraviolet (EUV) photolithography is seen as the key enabler for increasing transistor density in the next decade. In EUV lithography, 13.5 nm EUV light is illuminated through a reticle, holding a pattern to be printed, onto a silicon wafer. This process is performed about 100 times per wafer, at a rate of over a hundred wafers an hour. During this process, a certain percentage of the light energy is converted into heat in the wafer. In turn, this heat causes the wafer to deform which increases the overlay error, and as a result, reduces the manufacturing yield. To alleviate this, we propose a firm real-time control system that uses a wafer heat feed-forward model to compensate for the wafer deformation. The model calculates the expected wafer deformation, and then, compensates for that by adjusting the light projection and or the wafer movement. However, the model computational demands are very high. As a result, it needs to be executed on dedicated HW that can perform computations quickly. To this end, we deploy Graphics Processing Units (GPUs) to accelerate the calculations. In order to fit the computations within the required time budgets, we combine in a novel manner multiple techniques, such as compression and mixed-precision arithmetic, with recent advancements in GPUs to build a GPU-based real-time control system. A proof-of-concept implementation using NVIDIA P100 GPUs is able to deliver decompression throughput of 33 GB s and a sustained 198 GFLOP s per GPU for mixed-precision dense matrix-vector multiplication.
@cite_9 , the first real attempt in accelerating a real-time control loop (involving a particles filter) on GPUs is proposed. The proposed implementation was done on commercial GeForce cards and can be integrated in large industrial systems. Similar to our proposed solution, @cite_9 is targeting latencies below 50 ms. However, a key difference between @cite_9 and our solution is the model computational complexity and the amount of data needed to execute the model. Particle filters are compute-bound and require small amounts of data compared to the dense matrices involved in solving the 3D mechanical deformation equation described in .
@cite_9
1973636889 2129152507 1787735662 2119104235
We present the design, analysis, and real-time implementation of a distributed computation particle filter on a graphic processing unit (GPU) architecture that is especially suited for fast real-time control applications. The proposed filter architecture is composed of a number of local subfilters that can share limited information among each other via an arbitrarily chosen abstract connected communication topology. We develop a detailed implementation procedure for GPU architectures focusing on distributed resampling as a crucial step in our approach, and describe alternative methods in the literature. We analyze the role of the most important parameters such as the number of exchanged particles and the effect of the particle exchange topology. The significant speedup and increase in performance obtained with our framework with respect to both available GPU solutions and standard sequential CPU methods enable particle filter implementations in fast real-time feedback control systems. This is illustrated via experimental and simulation results using a real-time visual servoing problem of a robotic arm capable of running in closed loop with an update rate of 100 Hz, while performing particle filter calculations that involve over one million particles. Abstract This paper reports large-scale direct numerical simulations of homogeneous-isotropic fluid turbulence, achieving sustained performance of 1.08 petaflop s on gpu hardware using single precision. The simulations use a vortex particle method to solve the Navier–Stokes equations, with a highly parallel fast multipole method ( fmm ) as numerical engine, and match the current record in mesh size for this application, a cube of 4096 3 computational points solved with a spectral method. The standard numerical approach used in this field is the pseudo-spectral method, relying on the fft algorithm as the numerical engine. The particle-based simulations presented in this paper quantitatively match the kinetic energy spectrum obtained with a pseudo-spectral method, using a trusted code. In terms of parallel performance, weak scaling results show the fmm -based vortex method achieving 74 parallel efficiency on 4096 processes (one gpu per mpi process, 3 gpu s per node of the tsubame -2.0 system). The fft -based spectral method is able to achieve just 14 parallel efficiency on the same number of mpi processes (using only cpu cores), due to the all-to-all communication pattern of the fft algorithm. The calculation time for one time step was 108 s for the vortex method and 154 s for the spectral method, under these conditions. Computing with 69 billion particles, this work exceeds by an order of magnitude the largest vortex-method calculations to date. We develop an intermediate representation for deformable part models and show that this representation has favorable performance characteristics for multi-class problems when the number of classes is high. Our model uses sparse coding of part filters to represent each filter as a sparse linear combination of shared dictionary elements. This leads to a universal set of parts that are shared among all object classes. Reconstruction of the original part filter responses via sparse matrix-vector product reduces computation relative to conventional part filter convolutions. Our model is well suited to a parallel implementation, and we report a new GPU DPM implementation that takes advantage of sparse coding of part filters. The speed-up offered by our intermediate representation and parallel computation enable real-time DPM detection of 20 different object classes on a laptop computer. Advanced grasp control algorithms could benefit greatly from accurate tracking of the object as well as an accurate all-around knowledge of the system when the robot attempts a grasp. This motivates our study of the G-SL(AM)2 problem, in which two goals are simultaneously pursued: object tracking relative to the hand and estimation of parameters of the dynamic model. We view the G-SL(AM)2 problem as a filtering problem. Because of stick-slip friction and collisions between the object and hand, suitable dynamic models exhibit strong nonlinearities and jump discontinuities. This fact makes Kalman filters (which assume linearity) and extended Kalman filters (which assume differentiability) inapplicable, and leads us to develop a particle filter. An important practical problem that arises during grasping is occlusion of the view of the object by the robot's hand. To combat the resulting loss of visual tracking fidelity, we designed a particle filter that incorporates tactile sensor data. The filter is evaluated off-line with data gathered in advance from grasp acquisition experiments conducted with a planar test rig. The results show that our particle filter performs quite well, especially during periods of visual occlusion, in which it is much better than the same filter without tactile data.