Abstract: A method of slicing a deep learning model for a heterogeneous embedded system includes collecting, by a model slicing apparatus, an execution time and power consumption when each layer corresponding to one layer of a deep learning model including a plurality of layers is executed in each computing device of the heterogeneous embedded system, predicting, by the model slicing apparatus, a performance cost and a power cost when each of the layers is executed in each of the computing devices using the execution time and the power consumption, predicting, by the model slicing apparatus, a communication cost when transmitting information from each of the layers to a next layer in each of the computing devices, and slicing, by the model slicing apparatus, the plurality of layers so that different sliced layers are allocated to each of the computing devices based on the performance cost, the power cost, and the communication cost in a given execution time limit condition using a reinforcement learning model.

Abstract: A method and apparatus for partitioning memory bandwidth of a system determines state combinations of techniques for partitioning the memory bandwidth of the system based on at least one application program executed on cores of the system, determines candidate state combinations corresponding to characteristics of the application program, among the state combinations, by profiling the application program based on the state combinations, selects a final state combination for the application program by exploring a state space of the system corresponding to the candidate state combinations, and partitions the memory bandwidth of the system based on the final state combination.

Abstract: A method and apparatus for partitioning memory bandwidth of a system determines state combinations of techniques for partitioning the memory bandwidth of the system based on at least one application program executed on cores of the system, determines candidate state combinations corresponding to characteristics of the application program, among the state combinations, by profiling the application program based on the state combinations, selects a final state combination for the application program by exploring a state space of the system corresponding to the candidate state combinations, and partitions the memory bandwidth of the system based on the final state combination.