DW1525 WLAN DRIVER WINDOWS 7 KEYGEN
The processor of claim 11, wherein the one or more circuits are further to change precision of one or more weights and one or more activation values of the intermediate trained model using post-training quantization (PTQ), wherein PTQ comprises quantizing the one or more weights and the one or more activation values for all layers of the neural network and the last layer of the neural network.ġ3. The processor of claim 10, wherein the one or more circuits are further to change the precision of one or more weights and one or more activation values using quantization aware training (QAT), wherein QAT comprises quantizing the one or more weights and one or more activation values for all layers of the neural network except for the last layer of the neural network.ġ2. A processor, comprising: one or more circuits to: change precision of one or more weights and one or more activation values, while training a neural network, to generate an intermediate trained model and change precision of one or more weights and one or more activation values of the intermediate trained model during the training of at least one additional layer in addition to layers previously trained in the intermediate trained model to generate a trained model.ġ1. The method of claim 1, wherein changing precision of the one or more weights and one or more activation values to generate the first trained model is performed by applying quantization aware training (QAT), and wherein changing precision of the one or more weights and the one or more activation values to generate the second trained model is performed by applying post-training quantization (PTQ).ġ0.
The method of claim 1, further comprising: using the second trained model to perform object detection or image classification.ĩ. The method of claim 1, wherein the at least one additional layer of the neural network is outside of the subset of layers of the neural network and includes weights and activation values having finer granularity in representation than 8-bit quantization.Ĩ. The method of claim 1, wherein the second trained model comprises 8-bit integer numbers representing the one or more weights and the one or more activation values.ħ.
The method of claim 1, wherein the first trained model is a mixed-precision deep neural network (DNN) model.Ħ. The method of claim 3, further comprising: updating the portion of the neural network, during training, by using the calculated gradients in a backward-propagation pass.ĥ. The method of claim 2, further comprising: calculating gradients for the one or more weights and the one or more activation values using a straight-through estimation (STE).Ĥ. The method of claim 1, wherein the precision of the one or more weights and the one or more activation values are changed while training the portion of the neural network during a forward pass by using an absolute maximum value of the one or more weights and a running average of absolute maximum values of the one or more activation values over the training.ģ. A method, comprising: changing precision of one or more weights and one or more activation values of a portion of a neural network to generate a first trained model, wherein the portion of the neural network comprises a subset of layers of the neural network and changing precision of the one or more weights and the one or more activation values of the first trained model of at least one additional layer in addition to the subset of layers of the neural network to generate a second trained model.Ģ. Название: Atheros / Dell Wireless Lan Driver
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