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公开(公告)号：US07548892B2
公开(公告)日：2009-06-16
申请号：US11748474
申请日：2007-05-14
CPC分类号： G06N99/005 , G06N3/08
摘要： A system and method for processing machine learning techniques (such as neural networks) and other non-graphics applications using a graphics processing unit (GPU) to accelerate and optimize the processing. The system and method transfers an architecture that can be used for a wide variety of machine learning techniques from the CPU to the GPU. The transfer of processing to the GPU is accomplished using several novel techniques that overcome the limitations and work well within the framework of the GPU architecture. With these limitations overcome, machine learning techniques are particularly well suited for processing on the GPU because the GPU is typically much more powerful than the typical CPU. Moreover, similar to graphics processing, processing of machine learning techniques involves problems with solving non-trivial solutions and large amounts of data.
摘要翻译： 一种用于处理机器学习技术（例如神经网络）和使用图形处理单元（GPU）来加速和优化处理的其他非图形应用的系统和方法。 该系统和方法传输一种可用于从CPU到GPU的各种机器学习技术的架构。 处理到GPU的转移是通过克服这些限制并在GPU架构的框架内工作良好的几种新技术实现的。 由于克服了这些限制，机器学习技术特别适用于GPU上的处理，因为GPU通常比典型的CPU功能更强大。 此外，类似于图形处理，机器学习技术的处理涉及解决非平凡解决方案和大量数据的问题。
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2.发明授权System and method for accelerating and optimizing the processing of machine learning techniques using a graphics processing unit 有权用于加速和优化使用图形处理单元的机器学习技术的处理的系统和方法公开(公告)号：US07219085B2
公开(公告)日：2007-05-15
申请号：US10837382
申请日：2004-04-30
IPC分类号： G06F15/80
CPC分类号： G06K9/00986 , G06N3/08
摘要： A system and method for processing machine learning techniques (such as neural networks) and other non-graphics applications using a graphics processing unit (GPU) to accelerate and optimize the processing. The system and method transfers an architecture that can be used for a wide variety of machine learning techniques from the CPU to the GPU. The transfer of processing to the GPU is accomplished using several novel techniques that overcome the limitations and work well within the framework of the GPU architecture. With these limitations overcome, machine learning techniques are particularly well suited for processing on the GPU because the GPU is typically much more powerful than the typical CPU. Moreover, similar to graphics processing, processing of machine learning techniques involves problems with solving non-trivial solutions and large amounts of data.
摘要翻译： 一种用于处理机器学习技术（例如神经网络）和使用图形处理单元（GPU）来加速和优化处理的其他非图形应用的系统和方法。 该系统和方法传输一种可用于从CPU到GPU的各种机器学习技术的架构。 处理到GPU的转移是通过克服这些限制并在GPU架构的框架内工作良好的几种新技术实现的。 由于克服了这些限制，机器学习技术特别适用于GPU上的处理，因为GPU通常比典型的CPU功能更强大。 此外，类似于图形处理，机器学习技术的处理涉及解决非平凡解决方案和大量数据的问题。
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3.发明授权Optimizing performance of a graphics processing unit for efficient execution of general matrix operations 有权优化图形处理单元的性能，以有效执行一般矩阵运算公开(公告)号：US07567252B2
公开(公告)日：2009-07-28
申请号：US10877730
申请日：2004-06-25
IPC分类号： G06F15/00
CPC分类号： G06T1/20 , G06F17/16 , G06N3/063 , G06N99/005
摘要： A system and method for optimizing the performance of a graphics processing unit (GPU) for processing and execution of general matrix operations such that the operations are accelerated and optimized. The system and method describes the layouts of operands and results in graphics memory, as well as partitioning the processes into a sequence of passes through a macro step. Specifically, operands are placed in memory in a pattern, results are written into memory in a pattern appropriate for use as operands in a later pass, data sets are partitioned to insure that each pass fits into fixed sized memory, and the execution model incorporates generally reusable macro steps for use in multiple passes. These features enable greater efficiency and speed in processing and executing general matrix operations.
摘要翻译： 一种用于优化用于处理和执行一般矩阵运算的图形处理单元（GPU）的性能的系统和方法，使得加速和优化操作。 该系统和方法描述了图形存储器中的操作数和结果的布局，以及将进程划分为通过宏步骤的顺序。 具体来说，操作数以模式放置在存储器中，结果以适合在稍后传递中用作操作数的模式写入存储器，数据集被分区以确保每个通过符合固定大小的存储器，并且执行模型通常包含 可重复使用的宏步骤可用于多次通过。 这些特性使得在处理和执行通用矩阵运算时能够提高效率和速度。
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