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公开(公告)号:US12153930B2
公开(公告)日:2024-11-26
申请号:US17565391
申请日:2021-12-29
Applicant: Advanced Micro Devices, Inc.
Inventor: Hai Xiao
IPC: G06F9/30 , G06F9/355 , G06F9/38 , G06F18/214 , G06N3/04 , G06N3/048 , G06N3/063 , G06N3/08 , G06N3/084
Abstract: A processing device is provided which comprises memory configured to store data and a processor configured to execute a forward activation of the neural network using a low precision floating point (FP) format, scale up values of numbers represented by the low precision FP format and process the scaled up values of the numbers as non-zero values for the numbers. The processor is configured to scale up the values of one or more numbers, via scaling parameters, to a scaled up value equal to or greater than a floor of a dynamic range of the low precision FP format. The scaling parameters are, for example, static parameters or alternatively, parameters determined during execution of the neural network.
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公开(公告)号:US20230205544A1
公开(公告)日:2023-06-29
申请号:US17565391
申请日:2021-12-29
Applicant: Advanced Micro Devices, Inc.
Inventor: Hai Xiao
CPC classification number: G06F9/3887 , G06F9/3555 , G06K9/6256 , G06N3/04
Abstract: A processing device is provided which comprises memory configured to store data and a processor configured to execute a forward activation of the neural network using a low precision floating point (FP) format, scale up values of numbers represented by the low precision FP format and process the scaled up values of the numbers as non-zero values for the numbers. The processor is configured to scale up the values of one or more numbers, via scaling parameters, to a scaled up value equal to or greater than a floor of a dynamic range of the low precision FP format. The scaling parameters are, for example, static parameters or alternatively, parameters determined during execution of the neural network.
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公开(公告)号:US20230409868A1
公开(公告)日:2023-12-21
申请号:US17844204
申请日:2022-06-20
Applicant: Advanced Micro Devices, Inc.
Inventor: Hai Xiao , Adam H Li , Harris Eleftherios Gasparakis
Abstract: Activation scaled clipping layers for neural networks are described. An activation scaled clipping layer processes an output of a neuron in a neural network using a scaling parameter and a clipping parameter. The scaling parameter defines how numerical values are amplified relative to zero. The clipping parameter specifies a numerical threshold that causes the neuron output to be expressed as a value defined by the numerical threshold if the neuron output satisfies the numerical threshold. In some implementations, the scaling parameter is linear and treats numbers within a numerical range as being equivalent, such that any number in the range is scaled by a defined magnitude, regardless of value. Alternatively, the scaling parameter is nonlinear, which causes the activation scaled clipping layer to amplify numbers within a range by different magnitudes. Each scaling and clipping parameter is learnable during training of a machine learning model implementing the neural network.
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