Abstract: Disclosed is an apparatus and method.

Abstract: A method for training a machine learning force fields (MLFF) model, the method including obtaining, using the MLFF model, edge features corresponding to a training sample, wherein the training sample includes data related to a plurality of atoms, and the edge features represent relationship between edges of each atom among the plurality of atoms, computing a correlation loss corresponding to the edge features, wherein the correlation loss represents correlation between edge features corresponding to the plurality of atoms, updating parameters of the MLFF model based on the correlation loss to obtain a trained MLFF model, and generating, using the trained MLFF model, a molecular dynamics (MD) simulation based on an input sample.

Abstract: Disclosed is an apparatus and method.

Abstract: A method of operating a hardware accelerator, includes loading a lookup table, mapping each of input data values of input data to an index of indexes in the lookup table based on an input data distribution of the input data, and obtaining output data values corresponding to the input data values using the lookup table. The output data values are proportional to corresponding softmax values of the input data values.

Abstract: A method of operating a hardware accelerator, includes loading a lookup table, mapping each of input data values of input data to an index of indexes in the lookup table based on an input data distribution of the input data, and obtaining output data values corresponding to the input data values using the lookup table. The output data values are proportional to corresponding softmax values of the input data values.

Abstract: A random number generator includes a first oscillator configured to output a first oscillating signal having a first frequency. A second oscillator is configured to output a second oscillating signal having a second frequency different from the first frequency. A sampling unit is configured to receive the first and second oscillating signals. The sampling unit is configured to generate at least one entropy source by combining the received first and second oscillating signals. The sampling unit is configured to generate a random bit corresponding to the generated entropy source using a third oscillating signal. A third oscillator & control unit is configured to control the first and second oscillators and to generate the third oscillating signal. A frequency of the third oscillating signal is lower than the first and second frequencies.