Invention Grant
- Patent Title: Quantized neural network training and inference
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Application No.: US15478531Application Date: 2017-04-04
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Publication No.: US10831444B2Publication Date: 2020-11-10
- Inventor: Ran El-Yaniv , Itay Hubara , Daniel Soudry
- Applicant: Technion Research & Development Foundation Limited
- Applicant Address: IL Haifa
- Assignee: Technion Research & Development Foundation Limited
- Current Assignee: Technion Research & Development Foundation Limited
- Current Assignee Address: IL Haifa
- Main IPC: G06F7/523
- IPC: G06F7/523 ; G06N3/04 ; G06F7/48 ; G06N3/08

Abstract:
Training neural networks by constructing a neural network model having neurons each associated with a quantized activation function adapted to output a quantized activation value. The neurons are arranged in layers and connected by connections associated quantized connection weight functions adapted to output quantized connection weight values. During a training process a plurality of weight gradients are calculated during backpropagation sub-processes by computing neuron gradients, each of an output of a respective the quantized activation function in one layer with respect to an input of the respective quantized activation function. Each neuron gradient is calculated such that when an absolute value of the input is smaller than a positive constant threshold value, the respective neuron gradient is set as a positive constant output value and when the absolute value of the input is smaller than the positive constant threshold value the neuron gradient is set to zero.
Public/Granted literature
- US20170286830A1 QUANTIZED NEURAL NETWORK TRAINING AND INFERENCE Public/Granted day:2017-10-05
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