- 专利标题: CONVOLUTIONAL NEURAL NETWORK OPTIMIZATION MECHANISM
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申请号: US16283021申请日: 2019-02-22
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公开(公告)号: US20190188554A1公开(公告)日: 2019-06-20
- 发明人: Liwei Ma , Elmoustapha Ould-Ahmed-Vall , Barath Lakshmanan , Ben J. Ashbaugh , Jingyi Jin , Jeremy Bottleson , Mike B. Macpherson , Kevin Nealis , Dhawal Srivastava , Joydeep Ray , Ping T. Tang , Michael S. Strickland , Xiaoming Chen , Anbang Yao , Tatiana Shpeisman , Altug Koker , Abhishek R. Appu
- 申请人: Intel Corporation
- 申请人地址: US CA Santa Clara
- 专利权人: Intel Corporation
- 当前专利权人: Intel Corporation
- 当前专利权人地址: US CA Santa Clara
- 主分类号: G06N3/04
- IPC分类号: G06N3/04 ; G06N3/08 ; G06T1/20 ; G06N3/063
摘要:
Embodiments provide systems and methods which facilitate optimization of a convolutional neural network (CNN). One embodiment provides for a non-transitory machine-readable medium storing instructions that cause one or more processors to perform operations comprising processing a trained convolutional neural network (CNN) to generate a processed CNN, the trained CNN having weights in a floating-point format. Processing the trained CNN includes quantizing the weights in the floating-point format to generate weights in an integer format. Quantizing the weights includes generating a quantization table to enable non-uniform quantization of the weights and quantizing the weights from the floating-point format to the integer format using the quantization table. The operations additionally comprise performing an inference operation utilizing the processed CNN with the integer format weights.
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