The Experts below are selected from a list of 33 Experts worldwide ranked by ideXlab platform
Eihachiro Nakamae - One of the best experts on this subject based on the ideXlab platform.
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method of displaying optical effects within water using Accumulation Buffer
International Conference on Computer Graphics and Interactive Techniques, 1994Co-Authors: Tomoyuki Nishita, Eihachiro NakamaeAbstract:A precise shading model is required to display realistic images. Recently research on global illumination has been widespread. In global illumination, problems of diffuse reflection have been solved fairly well, but some optical problems after specular reflection and refraction still remain. Some natural phenomena stand out in reflected/refracted light from the wave surface of water. Refracted light from water surface converges and diverges, and creates shafts of light due to scattered light from particles. The color of the water is influenced by scattering/absorption effects of water molecules and suspensions. For these effects, the intensity and direction of incident light to particles plays an important role, and it is difficult to calculate them in conventional ray-tracing because light refracts when passing through waves. Therefore, the pre-processing tracing from light sources is necessary. The method proposed here can effectively calculate optical effects, shaft of light, caustics, and color of the water without such pre-processing by using a scanline Z-Buffer and Accumulation Buffer.
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SIGGRAPH - Method of displaying optical effects within water using Accumulation Buffer
Proceedings of the 21st annual conference on Computer graphics and interactive techniques - SIGGRAPH '94, 1994Co-Authors: Tomoyuki Nishita, Eihachiro NakamaeAbstract:A precise shading model is required to display realistic images. Recently research on global illumination has been widespread. In global illumination, problems of diffuse reflection have been solved fairly well, but some optical problems after specular reflection and refraction still remain. Some natural phenomena stand out in reflected/refracted light from the wave surface of water. Refracted light from water surface converges and diverges, and creates shafts of light due to scattered light from particles. The color of the water is influenced by scattering/absorption effects of water molecules and suspensions. For these effects, the intensity and direction of incident light to particles plays an important role, and it is difficult to calculate them in conventional ray-tracing because light refracts when passing through waves. Therefore, the pre-processing tracing from light sources is necessary. The method proposed here can effectively calculate optical effects, shaft of light, caustics, and color of the water without such pre-processing by using a scanline Z-Buffer and Accumulation Buffer.
Tomoyuki Nishita - One of the best experts on this subject based on the ideXlab platform.
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method of displaying optical effects within water using Accumulation Buffer
International Conference on Computer Graphics and Interactive Techniques, 1994Co-Authors: Tomoyuki Nishita, Eihachiro NakamaeAbstract:A precise shading model is required to display realistic images. Recently research on global illumination has been widespread. In global illumination, problems of diffuse reflection have been solved fairly well, but some optical problems after specular reflection and refraction still remain. Some natural phenomena stand out in reflected/refracted light from the wave surface of water. Refracted light from water surface converges and diverges, and creates shafts of light due to scattered light from particles. The color of the water is influenced by scattering/absorption effects of water molecules and suspensions. For these effects, the intensity and direction of incident light to particles plays an important role, and it is difficult to calculate them in conventional ray-tracing because light refracts when passing through waves. Therefore, the pre-processing tracing from light sources is necessary. The method proposed here can effectively calculate optical effects, shaft of light, caustics, and color of the water without such pre-processing by using a scanline Z-Buffer and Accumulation Buffer.
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SIGGRAPH - Method of displaying optical effects within water using Accumulation Buffer
Proceedings of the 21st annual conference on Computer graphics and interactive techniques - SIGGRAPH '94, 1994Co-Authors: Tomoyuki Nishita, Eihachiro NakamaeAbstract:A precise shading model is required to display realistic images. Recently research on global illumination has been widespread. In global illumination, problems of diffuse reflection have been solved fairly well, but some optical problems after specular reflection and refraction still remain. Some natural phenomena stand out in reflected/refracted light from the wave surface of water. Refracted light from water surface converges and diverges, and creates shafts of light due to scattered light from particles. The color of the water is influenced by scattering/absorption effects of water molecules and suspensions. For these effects, the intensity and direction of incident light to particles plays an important role, and it is difficult to calculate them in conventional ray-tracing because light refracts when passing through waves. Therefore, the pre-processing tracing from light sources is necessary. The method proposed here can effectively calculate optical effects, shaft of light, caustics, and color of the water without such pre-processing by using a scanline Z-Buffer and Accumulation Buffer.
Huazhong Yang - One of the best experts on this subject based on the ideXlab platform.
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FPGA - Compressed CNN Training with FPGA-based Accelerator
Proceedings of the 2019 ACM SIGDA International Symposium on Field-Programmable Gate Arrays, 2019Co-Authors: Kaiyuan Guo, Shuang Liang, Xuefei Ning, Yu Wang, Huazhong YangAbstract:Training convolutional neural network (CNN) usually requires large amount of computation resource, time and power. Researchers and cloud service providers in this region needs fast and efficient training system. GPU is currently the best candidate for CNN training. But FPGAs have already shown good performance and energy efficiency as CNN inference accelerators. In this work, we design a compressed training process together with an FPGA-based accelerator for energy efficient CNN training. We adopt two of the widely used model compression methods, quantization and pruning, to accelerate CNN training process. The difference between inference and training brought challenges to apply the two methods in training. First, training requires higher data precision. We use the gradient Accumulation Buffer to achieve low operation complexity while keeping gradient descent precision. Second, sparse network results in different types of functions in forward and back-propagation phases. We design a novel architecture to utilize both inference and back-propagation sparsity. Experimental results show that the proposed training process achieves similar accuracy compared with traditional training process with floating point data. The proposed accelerator achieves 641GOP/s equivalent performance and 2.86x better energy efficiency compared with GPU.
Alex Yen - One of the best experts on this subject based on the ideXlab platform.
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SIGGRAPH - Hardware accelerated rendering of antialiasing using a modified a-Buffer algorithm
Proceedings of the 24th annual conference on Computer graphics and interactive techniques - SIGGRAPH '97, 1997Co-Authors: Stephanie Winner, Michael W. Kelley, Brent Pease, Bill Rivard, Alex YenAbstract:one pass per subpixel sample) through the hardware rendering pipeline. The resulting image is very high quality, but the performance degrades in proportion to the number of subpixel samples used by the filter function. This paper describes algorithms for accelerating antialiasing in 3D graphics through low-cost custom hardware. The rendering architecture employs a multiple-pass algorithm to perform front-to-back hidden surface removal and shading. Coverage mask evaluation is used to composite objects in 3D. The key advantage of this approach is that antialiasing requires no additional memory and decreases rendering performance by only 30-40% for typical images. The system is image partition based and is scalable to satisfy a wide range of performance and cost constraints. An A-Buffer implementation does not require several passes of the object data, but does require sorting objects by depth before compositing them. The amount of memory required to store the sorted layers is limited to the number of subpixel samples, but it is significant since the color, opacity and mask data are needed for each layer. The compositing operation uses a blending function which is based on three possible subpixel coverage components and is more computationally intensive than the Accumulation Buffer blending function. The difficulty of implementing the A-Buffer algorithm in hardware is described by Molnar [12]. CR
Kaiyuan Guo - One of the best experts on this subject based on the ideXlab platform.
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FPGA - Compressed CNN Training with FPGA-based Accelerator
Proceedings of the 2019 ACM SIGDA International Symposium on Field-Programmable Gate Arrays, 2019Co-Authors: Kaiyuan Guo, Shuang Liang, Xuefei Ning, Yu Wang, Huazhong YangAbstract:Training convolutional neural network (CNN) usually requires large amount of computation resource, time and power. Researchers and cloud service providers in this region needs fast and efficient training system. GPU is currently the best candidate for CNN training. But FPGAs have already shown good performance and energy efficiency as CNN inference accelerators. In this work, we design a compressed training process together with an FPGA-based accelerator for energy efficient CNN training. We adopt two of the widely used model compression methods, quantization and pruning, to accelerate CNN training process. The difference between inference and training brought challenges to apply the two methods in training. First, training requires higher data precision. We use the gradient Accumulation Buffer to achieve low operation complexity while keeping gradient descent precision. Second, sparse network results in different types of functions in forward and back-propagation phases. We design a novel architecture to utilize both inference and back-propagation sparsity. Experimental results show that the proposed training process achieves similar accuracy compared with traditional training process with floating point data. The proposed accelerator achieves 641GOP/s equivalent performance and 2.86x better energy efficiency compared with GPU.
