Neural network computing system and method of executing neural network model

A neural network computing system comprising:

• a processor comprising heterogeneous computing devices that are configured to execute a neural network model;
• a memory configured to buffer input data and output data of the neural network model;
• a memory controller configured to control data input and data output of the memory;
• and
• a system bus configured to support communication between the processor and the memory controller,
• wherein the processor comprises:
• a feedforward control portion configured to:
• estimate, based on an input of static analysis data that is independent of a result of execution of the neural network model, an amount of work of each node from among a plurality of nodes included in the neural network model;
• and
• determine respective operating clock frequencies of hardware devices, including the heterogeneous computing devices, the system bus, and the memory controller, based on an input of a target end-to-end execution time of the neural network model and an input of the estimated amount of work of each node from among the plurality of nodes;
• a neural network model execution portion configured to control the hardware devices according to the determined respective operating clock frequencies, execute the neural network model, and output an actual end-to-end execution time of the neural network model;
• and
• a feedback control portion configured to adjust the respective operating clock frequencies of the hardware devices based on dynamic analysis data dependent on the result of the execution of the neural network model, including the actual end-to-end execution time, and
• wherein the respective operating clock frequencies of the hardware devices are lowest operating clock frequencies of the hardware devices, respectively, at which the hardware devices perform a predetermined amount of work according to the target end-to-end execution time.

A method of executing a neural network model, the method comprising:

• estimating an amount of work for each node among a plurality of nodes included in the neural network model based on execution of the neural network model being triggered;
• determining a target execution time for each node of the plurality of nodes based on a target end-to-end execution time of the neural network model;
• controlling an operating clock frequency of each of a plurality of heterogeneous computing devices for executing the neural network model, based on a target computing device for each node, the amount of work for each node, and the target execution time for each node;
• executing the neural network model using the plurality of heterogeneous computing devices operating according to the controlled operating clock frequency of each of the plurality of heterogeneous computing devices;
• and
• adjusting the operating clock frequency of each of the plurality of heterogeneous computing devices for executing the neural network model based on an actual end-to-end execution time of the neural network model,
• wherein the operating clock frequency is a lowest operating clock frequency of a respective one of the plurality of heterogeneous computing devices at which the respective one of the plurality of heterogeneous computing devices performs a predetermined amount of work according to the target end-to-end execution time.