Abstract: The present disclosure relates to a method for generating a program for use in an accelerator for deep learning. The method may include receiving, by a computing device, a deep learning application, generating an element-wise operation list included in the deep learning application, generating an intermediate expression from the element-wise operation list, and generating, based on the intermediate expression, a program for use in an accelerator for the deep learning application.

Abstract: A method for automatically optimizing a program based on reinforcement learning includes (a) receiving an input for a source program, which includes a fixed parameter and variable parameter, (b) generating the source program based on the received input, (c) converting the source program into an object program, (d) executing the converted object program to measure a performance of the executed object program, (e) inputting the variable parameter and the measured performance into a machine learning model, and outputting a variation of the variable parameter, and (f) regenerating a source program reflecting the variation of the variable parameter.

Abstract: A processor-implemented accelerator method includes: reading, from a memory, an instruction to be executed in an accelerator; reading, from the memory, input data based on the instruction; and performing, on the input data and a parameter value included in the instruction, an inference task corresponding to the instruction.

Abstract: A processor-implemented accelerator method includes: reading, from a memory, an instruction to be executed in an accelerator; reading, from the memory, input data based on the instruction; and performing, on the input data and a parameter value included in the instruction, an inference task corresponding to the instruction.

Abstract: A computing method and device for large-scale computing is provided. The computing device includes at least one processing device configured to perform an operation related to a neural network, a sensor configured to sense an electrical characteristic of the at least one processing device, an operating frequency of the at least one processing device, and a temperature of the at least one processing device, and a processor configured to calculate a workload to be allocated to the at least one processing device based on an operating mode of the at least one processing device, the electrical characteristic of the at least one processing device, the operating frequency of the at least one processing device, and the temperature of the at least one processing device, and control the electrical characteristic, the operating frequency, and the temperature based on the operating mode and the workload.

Abstract: The present disclosure relates to a method for generating a program for use in an accelerator for deep learning. The method may include receiving, by a computing device, a deep learning application, generating an element-wise operation list included in the deep learning application, generating an intermediate expression from the element-wise operation list, and generating, based on the intermediate expression, a program for use in an accelerator for the deep learning application.

Abstract: The present disclosure relates to a method for automatically optimizing a program based on reinforcement learning. The method for automatically optimizing a program based on reinforcement learning includes (a) receiving an input for a source program, which includes a fixed parameter and variable parameter, (b) generating the source program based on the received input, (c) converting the source program into an object program, (d) executing the converted object program to measure a performance of the executed object program, (e) inputting the variable parameter and the measured performance into a machine learning model, and outputting a variation of the variable parameter, and (f) regenerating a source program reflecting the variation of the variable parameter.

Abstract: A processor-implemented accelerator method includes: reading, from a memory, an instruction to be executed in an accelerator; reading, from the memory, input data based on the instruction; and performing, on the input data and a parameter value included in the instruction, an inference task corresponding to the instruction.