Abstract: A learning method of a neural network system includes preparing a second neural network having the same weights as a first neural network which is pre-trained; adding noise to weights of the first neural network; generating a first output data of the first neural network and generating a second output data of the second neural network by providing input data to the first neural network and the second neural network; and calculating a loss function using the first output data, the second output data, and a true value corresponding to the input data.

Abstract: A computation circuit includes a plurality of first operation circuits; a plurality of quantization circuits configured to quantize outputs of the plurality of first operation circuits, respectively; a plurality of second operation circuits configured to perform operations on outputs of the plurality of quantization circuits, respectively; and an adder circuit configured to perform element wise addition operation on outputs of the plurality of second operation circuits.

Abstract: A memory expansion device includes an expansion control circuit configured to receive a near data processing (NDP) request and a remote memory device configured to store data corresponding to the NDP request according to control by the expansion control circuit. In response to the NDP request, the expansion control circuit performs a request processing operation to perform a read or a write operation on the remote memory device and performs a computation operation using the data corresponding to the NDP request.

Abstract: A host device includes a unit processor configured to generate a near data processing (NDP) request, a host expansion control circuit configured to receive the NDP request; and a local memory device configured to store data corresponding to the NDP request according to control by the expansion control circuit. In response to receiving the NDP request, the host expansion control circuit performs a request processing operation to perform a read or a write operation corresponding to the NDP request on the local memory device and performs a computation operation using the requested data corresponding to the NDP request.

Abstract: Provided are a neural network method and an apparatus, the method including obtaining a set of floating point data processed in a layer included in a neural network, determining a weighted entropy based on data values included in the set of floating point data, adjusting quantization levels assigned to the data values based on the weighted entropy, and quantizing the data values included in the set of floating point data in accordance with the adjusted quantization levels.

Abstract: Provided are a neural network method and an apparatus, the method including obtaining a set of floating point data processed in a layer included in a neural network, determining a weighted entropy based on data values included in the set of floating point data, adjusting quantization levels assigned to the data values based on the weighted entropy, and quantizing the data values included in the set of floating point data in accordance with the adjusted quantization levels.

Abstract: Provided are a neural network method and an apparatus, the method including obtaining a set of floating point data processed in a layer included in a neural network, determining a weighted entropy based on data values included in the set of floating point data, adjusting quantization levels assigned to the data values based on the weighted entropy, and quantizing the data values included in the set of floating point data in accordance with the adjusted quantization levels.