Abstract: A neural network operation apparatus and method are disclosed. The neural network operation apparatus may include an adder configured to perform addition of data for performing a neural network operation and main data, a first multiplexer configured to output one of an output result of the adder and the main data based on a reset signal, a second multiplexer configured to output one of the main data and a quantization result of the data based on a phase signal, and a controller configured to control the first and second multiplexers by generating the reset signal and the phase signal.

Abstract: A compile method for a neural network, the compile method includes receiving data related to the neural network, generating a grouped layer by grouping layers comprised in the neural network based on the data, generating a set of passes executable in parallel based on a dependency between a plurality of passes to process the neural network, generating a set of threads performing a plurality of optimization functions based on whether optimization operations performed by the optimization functions is performed independently for the layers, respectively, or sequentially based on a dependency between the layers, and performing compilation in parallel based on the grouped layer, the set of passes, and the set of threads.

Abstract: A compile method for a neural network, the compile method includes receiving data related to the neural network, generating a grouped layer by grouping layers comprised in the neural network based on the data, generating a set of passes executable in parallel based on a dependency between a plurality of passes to process the neural network, generating a set of threads performing a plurality of optimization functions based on whether optimization operations performed by the optimization functions is performed independently for the layers, respectively, or sequentially based on a dependency between the layers, and performing compilation in parallel based on the grouped layer, the set of passes, and the set of threads.

Abstract: A neural network operation apparatus and method are provided. The neural network operation apparatus includes an internal storage configured to store data to perform a neural network operation, an arithmetic logical unit (ALU) configured to perform an operation between the stored data and main data based on an operation control signal, an adder configured to add an output of the ALU and an output of a first multiplexer, wherein the first multiplexer is configured to output one of an output of the adder and the output of the ALU based on a reset signal, a second multiplexer configured to output one of the main data and a quantization result of the stored data based on a phase signal, and a controller configured to control the ALU, the first multiplexer, and the second multiplexer based on the operation control signal, the reset signal, and the phase signal.

Abstract: A neural network operation apparatus and method are disclosed. The neural network operation apparatus may include an adder configured to perform addition of data for performing a neural network operation and main data, a first multiplexer configured to output one of an output result of the adder and the main data based on a reset signal, a second multiplexer configured to output one of the main data and a quantization result of the data based on a phase signal, and a controller configured to control the first and second multiplexers by generating the reset signal and the phase signal.

Abstract: A compile method for a neural network, the compile method includes receiving data related to the neural network, generating a grouped layer by grouping layers comprised in the neural network based on the data, generating a set of passes executable in parallel based on a dependency between a plurality of passes to process the neural network, generating a set of threads performing a plurality of optimization functions based on whether optimization operations performed by the optimization functions is performed independently for the layers, respectively, or sequentially based on a dependency between the layers, and performing compilation in parallel based on the grouped layer, the set of passes, and the set of threads.

Abstract: A neural network operation method and apparatus are disclosed, where the network operation method including receiving data for a neural network operation, determining whether a size of the data is less than or equal to a threshold, generating stacked data by stacking a portion of the data based on the determining, and performing the neural network operation in parallel based on the stacked data.

Abstract: A neural network operation apparatus and method is provided. The neural network operation apparatus includes a memory configured to store data for a neural network operation, and a processor configured to validate the data based on a determination that the neural network operation should be performed on the data, obtain a real memory address to perform the neural network operation based on a result of the validating and a virtual tensor address of the data, and perform the neural network operation based on the real memory address.

Abstract: A method and apparatus for scheduling a neural network operation. The method includes receiving data on a layer of a neural network, generating partitions to be assigned to cores by dividing the data, generating tiles by dividing the partitions, and scheduling an operation order of the tiles based on whether the data are shared between the cores.

Abstract: A neural network operation apparatus includes: a receiver configured to receive a first input feature map; a controller configured to control multiplier-accumulators (MACs) included in a first MAC array; and a first operation engine comprising the first MAC array and configured to process the first input feature map based on the MACs of which operation states are controlled.