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1.发明授权Circuit and method for identifying exception cases in a floating-point unit and graphics processing unit employing the same 有权用于识别浮点单元中的异常情况的电路和方法以及采用该异常情况的图形处理单元公开(公告)号：US09448806B2
公开(公告)日：2016-09-20
申请号：US13626104
申请日：2012-09-25
Applicant: Nvidia Corporation
Inventor： Marcin Andrychowicz , Alex Fit-Florea
CPC classification number: G06F9/3861 , G06F9/30014 , G06F9/30181
Abstract: A floating-point unit and a method of identifying exception cases in a floating-point unit. In one embodiment, the floating-point unit includes: (1) a floating-point computation circuit having a normal path and an exception path and operable to execute an operation on an operand and (2) a decision circuit associated with the normal path and the exception path and configured to employ a flush-to-zero mode of the floating-point unit to determine which one of the normal path and the exception path is appropriate for carrying out the operation on the operand.
Abstract translation: 一个浮点单元和一种识别浮点单元中异常情况的方法。 在一个实施例中，浮点单元包括：（1）具有正常路径和异常路径的浮点计算电路，并且可操作以对操作数执行操作，以及（2）与正常路径相关联的判定电路， 所述异常路径并被配置为采用所述浮点单元的刷新至零模式来确定所述正常路径和所述异常路径中的哪一个适合于对所述操作数执行所述操作。
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公开(公告)号：US20230418726A1
公开(公告)日：2023-12-28
申请号：US17850847
申请日：2022-06-27
Applicant: NVIDIA Corporation
Inventor： Szymon Migacz , Pawel Morkisz , Alex Fit-Florea , Maciej Bala , Jakub Zakrzewski , Trivikram Krishnamurthy , Nitin Nitin , Sangkug Lym , Shang Wang , Chenhan Yu , Alexandre Milesi
IPC: G06F11/36
CPC classification number: G06F11/3612
Abstract: Methods and systems for comparing information obtained during execution of a workload to a set of inefficiency patterns, and determining the workload includes a potential inefficiency when the information matches at least one of the set of inefficiency patterns.
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公开(公告)号：US10997492B2
公开(公告)日：2021-05-04
申请号：US15838273
申请日：2017-12-11
Applicant: NVIDIA Corporation
Inventor： Szymon Migacz , Hao Wu , Dilip Sequeira , Ujval Kapasi , Maxim Milakov , Slawomir Kierat , Zacky Zhou , Yilin Zhang , Alex Fit-Florea
Abstract: Aspects of the present invention are directed to computer-implemented techniques for performing data compression and conversion between data formats of varying degrees of precision, and more particularly for improving the inferencing (application) of artificial neural networks using a reduced precision (e.g., INT8) data format. Embodiments of the present invention generate candidate conversions of data output, then employ a relative measure of quality to identify the candidate conversion with the greatest accuracy (i.e., least divergence from the original higher precision values). The representation can be then be used during inference to perform computations on the resulting output data.
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公开(公告)号：US12299577B2
公开(公告)日：2025-05-13
申请号：US15624577
申请日：2017-06-15
Applicant: NVIDIA CORPORATION
Inventor： Boris Ginsburg , Sergei Nikolaev , Ahmad Kiswani , Hao Wu , Amir Gholaminejad , Slawomir Kierat , Michael Houston , Alex Fit-Florea
Abstract: Aspects of the present invention are directed to computer-implemented techniques for improving the training of artificial neural networks using a reduced precision (e.g., float16) data format. Embodiments of the present invention rescale tensor values prior to performing matrix operations (such as matrix multiplication or matrix addition) to prevent overflow and underflow. To preserve accuracy throughout the performance of the matrix operations, the scale factors are defined using a novel data format to represent tensors, wherein a matrix is represented by the tuple X, where X=(a, v[.]), wherein a is a float scale factor and v[.] are scaled values stored in the float16 format. The value of any element X[i] according to this data format would be equal to a*v[i].
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公开(公告)号：US20180211152A1
公开(公告)日：2018-07-26
申请号：US15838273
申请日：2017-12-11
Applicant: NVIDIA Corporation
Inventor： Szymon Migacz , Hao Wu , Dilip Sequeira , Ujval Kapasi , Maxim Milakov , Slawomir Kierat , Zacky Zhou , Yilin Zhang , Alex Fit-Florea
CPC classification number: G06N3/04 , G06N3/0454 , G06N3/08 , G06N7/00
Abstract: Aspects of the present invention are directed to computer-implemented techniques for performing data compression and conversion between data formats of varying degrees of precision, and more particularly for improving the inferencing (application) of artificial neural networks using a reduced precision (e.g., INT8) data format. Embodiments of the present invention generate candidate conversions of data output, then employ a relative measure of quality to identify the candidate conversion with the greatest accuracy (i.e., least divergence from the original higher precision values). The representation can be then be used during inference to perform computations on the resulting output data.
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Patent Agency Ranking
公开(公告)号：US20200167125A1
公开(公告)日：2020-05-28
申请号：US16200325
申请日：2018-11-26
Applicant: NVIDIA Corporation
Inventor： Alex Fit-Florea , Boris Ginsburg , Pooya Davoodi , Amir Gholaminejad
Abstract: A method, computer readable medium, and system are disclosed for rounding floating point values. Dynamic directional rounding is a rounding technique for floating point operations. A floating point operation (addition, subtraction, multiplication, etc.) is performed on an operand to compute a floating point result. A sign (positive or negative) of the operand is identified. In one embodiment, the sign determines a direction in which the floating point result is rounded (towards negative or positive infinity). When used for updating parameters of a neural network during backpropagation, dynamic directional rounding ensures that rounding is performed in the direction of the gradient.
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7.发明申请CIRCUIT AND METHOD FOR IDENTIFYING EXCEPTION CASES IN A FLOATING-POINT UNIT AND GRAPHICS PROCESSING UNIT EMPLOYING THE SAME 有权用于识别浮点单元中的例外情况的电路和方法以及使用它的图形处理单元公开(公告)号：US20140089644A1
公开(公告)日：2014-03-27
申请号：US13626104
申请日：2012-09-25
Applicant: NVIDIA CORPORATION
Inventor： Marcin Andrychowicz , Alex Fit-Florea
CPC classification number: G06F9/3861 , G06F9/30014 , G06F9/30181
Abstract: A floating-point unit and a method of identifying exception cases in a floating-point unit. In one embodiment, the floating-point unit includes: (1) a floating-point computation circuit having a normal path and an exception path and operable to execute an operation on an operand and (2) a decision circuit associated with the normal path and the exception path and configured to employ a flush-to-zero mode of the floating-point unit to determine which one of the normal path and the exception path is appropriate for carrying out the operation on the operand.
Abstract translation: 一个浮点单元和一种识别浮点单元中异常情况的方法。 在一个实施例中，浮点单元包括：（1）具有正常路径和异常路径的浮点计算电路，并且可操作以对操作数执行操作，以及（2）与正常路径相关联的判定电路， 所述异常路径并被配置为采用所述浮点单元的刷新至零模式来确定所述正常路径和所述异常路径中的哪一个适合于对所述操作数执行所述操作。
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公开(公告)号：US20210256348A1
公开(公告)日：2021-08-19
申请号：US17306171
申请日：2021-05-03
Applicant: NVIDIA Corporation
Inventor： Szymon Migacz , Hao Wu , Dilip Sequeira , Ujval Kapasi , Maxim Milakov , Slawomir Kierat , Zacky Zhou , Yilin Zhang , Alex Fit-Florea
Abstract: Aspects of the present invention are directed to computer-implemented techniques for performing data compression and conversion between data formats of varying degrees of precision, and more particularly for improving the inferencing (application) of artificial neural networks using a reduced precision (e.g., INT8) data format. Embodiments of the present invention generate candidate conversions of data output, then employ a relative measure of quality to identify the candidate conversion with the greatest accuracy (i.e., least divergence from the original higher precision values). The representation can be then be used during inference to perform computations on the resulting output data.
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公开(公告)号：US20210232366A1
公开(公告)日：2021-07-29
申请号：US17163855
申请日：2021-02-01
Applicant: NVIDIA Corporation
Inventor： Alex Fit-Florea , Boris Ginsburg , Pooya Davoodi , Amir Gholaminejad
Abstract: A method, computer readable medium, and system are disclosed for rounding floating point values. Dynamic directional rounding is a rounding technique for floating point operations. A floating point operation (addition, subtraction, multiplication, etc.) is performed on an operand to compute a floating point result. A sign (positive or negative) of the operand is identified. In one embodiment, the sign determines a direction in which the floating point result is rounded (towards negative or positive infinity). When used for updating parameters of a neural network during backpropagation, dynamic directional rounding ensures that rounding is performed in the direction of the gradient.
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公开(公告)号：US10908878B2
公开(公告)日：2021-02-02
申请号：US16200325
申请日：2018-11-26
Applicant: NVIDIA Corporation
Inventor： Alex Fit-Florea , Boris Ginsburg , Pooya Davoodi , Amir Gholaminejad
Abstract: A method, computer readable medium, and system are disclosed for rounding floating point values. Dynamic directional rounding is a rounding technique for floating point operations. A floating point operation (addition, subtraction, multiplication, etc.) is performed on an operand to compute a floating point result. A sign (positive or negative) of the operand is identified. In one embodiment, the sign determines a direction in which the floating point result is rounded (towards negative or positive infinity). When used for updating parameters of a neural network during backpropagation, dynamic directional rounding ensures that rounding is performed in the direction of the gradient.
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