1. A data processing device comprising:
a data processing unit for processing input data using a neural network;
a compression controlling unit for determining quantization steps and generating quantization information that defines the quantization steps, the quantization steps being used when parameter data of the neural network is quantized; and
an encoding unit for encoding network configuration information and the quantization information to generate compressed data, the network configuration information including the parameter data quantized using the quantization steps determined by the compression controlling unit.
2. A data processing device comprising:
a data processing unit for processing input data using a neural network; and a decoding unit for decoding compressed data obtained by encoding quantization information and network configuration information, the quantization information defining quantization steps used when parameter data of the neural network is quantized, and the network configuration information including the parameter data quantized using the quantization steps in the quantization information, wherein
the data processing unit inversely quantizes the parameter data using the quantization information and the network configuration information which are decoded from the compressed data by the decoding unit, and constructs the neural network using the network configuration information including the inversely quantized parameter data.

3. The data processing device according to claim 1 or 2, wherein the parameter data of the neural network is weight information assigned to edges that connect nodes in the neural network.
4. The data processing device according to claim 1, wherein
the compression controlling unit changes the quantization steps on an edge-by-edge basis, and
the encoding unit encodes the quantization information that defines the edge-by-edge quantization steps.
5. The data processing device according to claim 1, wherein
the compression controlling unit changes the quantization steps on a node-by-node or kernel-by-kernel basis, and
the encoding unit encodes the quantization information that defines the node-by-node or kernel-by-kernel quantization steps.
6. The data processing device according to claim 1, wherein
the compression controlling unit changes the quantization steps on a layer-by-layer basis of the neural network, and
the encoding unit encodes the quantization information that defines the layer-by-layer quantization steps for the neural network.
7. A data processing method comprising:
a step of, by a decoding unit, decoding compressed data obtained by encoding quantization information and network configuration information, the quantization

information defining quantization steps used when parameter data of a neural network is quantized, the network configuration information including the parameter data quantized using the quantization steps in the quantization information; and
a step of, by a data processing unit, inversely quantizing the parameter data using the quantization information and the network configuration information which are decoded from the compressed data by the decoding unit, constructing the neural network using the network configuration information including the inversely quantized parameter data, and processing input data using the neural network.
8. Compressed data obtained by encoding
quantization information that defines quantization steps used when parameter data of a neural network is quantized; and
network configuration information including the parameter data quantized using the quantization steps in the quantization information, wherein
the compressed data causes a data processing device to inversely quantize the parameter data using the quantization information and the network configuration information which are decoded from the compressed data by the data processing device, and to construct the neural network using the network configuration information including the inversely quantized parameter data.