Samsung Electronics Co., Ltd.

Yen-kai Lin

Jing Wang

Woosung Choi

Yuanchen Chu

A method includes providing an artificial neural network comprising a compute-in-memory accelerator, the artificial neural network further comprising a hidden layer including a first plurality of artificial neurons; and training the artificial neural network using a bitwise modified rectified linear unit activation function for ones of the first plurality of artificial neurons, the bitwise modified rectified linear unit activation function comprising a bit activation function, which is configured to generate an output that is proportional to an input when the input is less than a critical threshold and configured to generate an output that is independent of the input when the input is greater than the critical threshold, wherein the input comprises a sum, across a second plurality of artificial neurons of a preceding layer of the artificial neural network having a plurality of weights associated therewith, respectively, of a product of an output from a respective one of the second plurality of artificial neurons and one bit of a respective one of the plurality of weights.

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San Jose, CA, United States of America

Yen-kai Lin