Invention Grant
- Patent Title: Non-uniform quantization of pre-trained deep neural network
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Application No.: US16181326Application Date: 2018-11-05
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Publication No.: US11348009B2Publication Date: 2022-05-31
- Inventor: Hui Chen , Ilia Ovsiannikov
- Applicant: Samsung Electronics Co., Ltd.
- Applicant Address: KR Suwon-si
- Assignee: Samsung Electronics Co., Ltd.
- Current Assignee: Samsung Electronics Co., Ltd.
- Current Assignee Address: KR Suwon-si
- Agency: Renaissance IP Law Group LLP
- Main IPC: G06N3/08
- IPC: G06N3/08 ; G06N3/04

Abstract:
A system and a method of quantizing a pre-trained neural network, includes determining by a layer/channel bit-width determiner for each layer or channel of the pre-trained neural network a minimum quantization noise for the layer or the channel for each master bit-width value in a predetermined set of master bit-width values; and selecting by a bit-width selector for the layer or the channel the master bit-width value having the minimum quantization noise for the layer or the channel. In one embodiment, the minimum quantization noise for the layer or the channel is based on a square of a range of weights for the layer or the channel that is multiplied by a constant to a negative power of a current master bit-width value.
Public/Granted literature
- US20200097823A1 NON-UNIFORM QUANTIZATION OF PRE-TRAINED DEEP NEURAL NETWORK Public/Granted day:2020-03-26
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