Samsung Electronics Co., Ltd.

Do-yun Kim

Sang-hyuck Ha

Jong-han Lim

Nak-woo Sung

Byeoung-su Kim

Han-young Yim

Ji-Hye Lee

Jun-Seok Park

In-yup Kang

Kwang-Hoon Kim

Jae-Gon Kim

Sung-Bo Shim

Kyoung-young Kim

Dong-Chul Ihm

Ki-Wook Song

Woo-Young Cheon

An artificial neural network (ANN) quantization method for generating an output ANN by quantizing an input ANN includes: obtaining second parameters by quantizing first parameters of the input ANN; obtaining a sample distribution from an intermediate ANN in which the obtained second parameters have been applied to the input ANN; and obtaining a fractional length for the sample distribution by quantizing the obtained sample distribution.

Method and apparatus for quantizing artificial neural network

An artificial neural network (ANN) system includes a processor, a virtual overflow detection circuit and a data format controller. The processor performs node operations with respect to a plurality of nodes included in each layer of an ANN to obtain a plurality of result values of the node operations and performs a quantization operation on the obtained plurality of result values based on a k-th fixed-point format for a current quantization of the each layer to obtain a plurality of quantization values. The virtual overflow detection circuit generates a virtual overflow information indicating a distribution of valid bit numbers of the obtained plurality of quantization values. The data format controller determines a (k+1)-th fixed-point format for a next quantization of the each layer based on the generated virtual overflow information. An overflow and/or an underflow are prevented efficiently by controlling the fixed-point format using the virtual overflow.

Artificial neural network and method of controlling fixed point in the same

A method and apparatus for generating a fixed point neural network are provided. The method includes selecting at least one layer of a neural network as an object layer, wherein the neural network includes a plurality of layers, each of the plurality of layers corresponding to a respective one of plurality of quantization parameters; forming a candidate parameter set including candidate parameter values with respect to a quantization parameter of the plurality of quantization parameters corresponding to the object layer; determining an update parameter value from among the candidate parameter values based on levels of network performance of the neural network, wherein each of the levels of network performance correspond to a respective one of the candidate parameter values; and updating the quantization parameter with respect to the object layer based on the update parameter value.

Method and apparatus for generating fixed point neural network

A method of generating a fixed-point type neural network by quantizing a floating-point type neural network, includes obtaining, by a device, a plurality of post-activation values by applying an activation function to a plurality of activation values that are received from a layer included in the floating-point type neural network, and deriving, by the device, a plurality of statistical characteristics for at least some of the plurality of post-activation values. The method further includes determining, by the device, a step size for the quantizing of the floating-point type neural network, based on the plurality of statistical characteristics, and determining, by the device, a final fraction length for the fixed-point type neural network, based on the step size.

Method and apparatus for generating fixed-point type neural network

A method of predicting a shape of a semiconductor device includes implementing a modeled semiconductor shape with respect to a designed semiconductor layout, extracting a plurality of samples by independently linearly combining process variables with respect to the modeled semiconductor shape; generating virtual spectrums with respect to ones of the extracted plurality of samples through optical analysis, indexing the virtual spectrums to produce indexed virtual spectrums, generating a shape prediction model by using the indexed virtual spectrums as an input and the modeled semiconductor shape as an output, and indexing a spectrum measured from a manufactured semiconductor device and inputting the spectrum to the shape prediction model to predict a shape of the manufactured semiconductor device.

Method of predicting shape of semiconductor device

A method of quantizing an artificial neural network includes dividing an input distribution of the artificial neural network into a plurality of segments, generating an approximated density function by approximating each of the plurality of segments, calculating at least one quantization error corresponding to at least one step size for quantizing the artificial neural network, based on the approximated density function, and determining a final step size for quantizing the artificial neural network based on the at least one quantization error.

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Gwacheon-si, South Korea

Do-yun Kim