Abstract: The present disclosure relates to a dataflow optimization method for low-power operation of a multicore system, the dataflow optimization method including: a step (a) of creating an FSM including a plurality of system states in consideration of dynamic factors that trigger a transition in system states for original dataflow; and a step (b) of optimizing the original dataflow through optimization of the created FSM.

Abstract: An apparatus and a method for optimizing a satellite system considering a hard error stability and a soft error stability are disclosed. The satellite system optimizing method which considers a hard error stability and a soft error stability according to an exemplary embodiment of the present disclosure includes acquiring hardware information of a processor which is loaded in the satellite system; acquiring workload information including a task which is performed by the processor; establishing a scheduling policy for the task based on the hardware information and the workload information; and quantifying a soft error stability and a hard error stability in accordance with the scheduling policy.

Abstract: A real-time scheduling apparatus and method for suppressing battery aging of a satellite system are disclosed. A real-time scheduling method for suppressing battery aging of a satellite system according to an exemplary embodiment of the present disclosure may include acquiring task information including a request period and an execution time of each of a plurality of tasks which is performed in the satellite system; determining an execution limit range and an execution order for each of the plurality of tasks which satisfies a predetermined real-time constraint based on the task information; and determining an optimal execution timing within the execution limit range of each of the plurality of tasks, based on the execution order and consumed current information of each of the plurality of tasks.

Abstract: An apparatus and a method for optimizing a satellite system considering a hard error stability and a soft error stability are disclosed. The satellite system optimizing method which considers a hard error stability and a soft error stability according to an exemplary embodiment of the present disclosure includes acquiring hardware information of a processor which is loaded in the satellite system; acquiring workload information including a task which is performed by the processor; establishing a scheduling policy for the task based on the hardware information and the workload information; and quantifying a soft error stability and a hard error stability in accordance with the scheduling policy.

Abstract: A real-time scheduling apparatus and method for suppressing battery aging of a satellite system are disclosed. A real-time scheduling method for suppressing battery aging of a satellite system according to an exemplary embodiment of the present disclosure may include acquiring task information including a request period and an execution time of each of a plurality of tasks which is performed in the satellite system; determining an execution limit range and an execution order for each of the plurality of tasks which satisfies a predetermined real-time constraint based on the task information; and determining an optimal execution timing within the execution limit range of each of the plurality of tasks, based on the execution order and consumed current information of each of the plurality of tasks.

Abstract: Disclosed are convolutional neural network-based road detecting apparatus and method and a convolutional neural network-based road detecting method according to an exemplary embodiment of the present disclosure includes applying a dilation operation to a ground truth for a road image of a learning image, training an inference model which detects a road region from a satellite image based on the learning image and the ground truth to which the dilation operation is applied, and receiving a prediction target image and generating an original segmentation map in which a road region is detected from the prediction target image by means of the inference model.

Abstract: The present disclosure relates to an apparatus and method for improving thermal cycling reliability of a multicore microprocessor, and a method for a method for improving thermal cycling reliability of a multicore microprocessor according to an embodiment of the present disclosure includes determining an optimal temperature of a microprocessor to maximize a mean time to failure of the microprocessor, and increasing at least one of an operating frequency of the microprocessor or a processor utilization of the microprocessor to make a temperature of the microprocessor equal to or higher than the optimal temperature.

Abstract: Disclosed are an apparatus and a method of multi-phase pruning a neural network with multi-sparsity levels and an SIMD-based neural network pruning method, and the SIMD-based neural network pruning method according to an exemplary embodiment of the present disclosure includes GEMM-transforming an internode weight kernel applied to a layer in a neural network; and pruning the GEMM-transformed weight kernel with a predetermined SIMD width as a unit.

Abstract: The present disclosure relates to an apparatus and method for improving thermal cycling reliability of a multicore microprocessor, and a method for a method for improving thermal cycling reliability of a multicore microprocessor according to an embodiment of the present disclosure includes determining an optimal temperature of a microprocessor to maximize a mean time to failure of the microprocessor, and increasing at least one of an operating frequency of the microprocessor or a processor utilization of the microprocessor to make a temperature of the microprocessor equal to or higher than the optimal temperature.

Abstract: The present disclosure relates to a dataflow optimization method for low-power operation of a multicore system, the dataflow optimization method including: a step (a) of creating an FSM including a plurality of system states in consideration of dynamic factors that trigger a transition in system states for original dataflow; and a step (b) of optimizing the original dataflow through optimization of the created FSM.