A method performed by a device processor for optimizing a deep learning operator, comprising: calling (S101, S203) a method of reading an image object to read target data from an L1 cache of an image processor to the device processor in response to detecting the target data in the L1 cache, wherein the target data comprises output data that was outputted from a network layer previous to a current network layer and that is to be inputted into the current network layer of a neural network model, a weight of the current network layer, and an offset value of the current network layer, wherein each of the output data, the weight, and the offset value is fixed-point data having a first preset number of bits, wherein the first preset number of bits is 8 or 16, wherein the target data is obtained by means of a first quantization operation performed on data to be quantized; performing (S102, S204) a second quantization operation in the device processor to obtain an operation result; and writing (S103, S205) the operation result into the main memory of the image processor; wherein performing (S102, S204) the second quantization operation in the device processor to obtain the operation result comprises: performing an operation on the output data based on the weight to obtain a first operation value and obtaining a cumulative result of the first operation value and the offset value, wherein the cumulative result is fixed-point data having a second preset number of bits, and the second preset number of bits is 32; and performing the second quantization operation on the cumulative result to obtain the operation result, wherein the operation result is fixed-point data having the first preset number of bits. calling (S101, S203) a method of reading an image object to read target data from an L1 cache of an image processor to the device processor in response to detecting the target data in the L1 cache, wherein the target data comprises output data that was outputted from a network layer previous to a current network layer and that is to be inputted into the current network layer of a neural network model, a weight of the current network layer, and an offset value of the current network layer, wherein each of the output data, the weight, and the offset value is fixed-point data having a first preset number of bits, wherein the first preset number of bits is 8 or 16, wherein the target data is obtained by means of a first quantization operation performed on data to be quantized; performing (S102, S204) a second quantization operation in the device processor to obtain an operation result; and writing (S103, S205) the operation result into the main memory of the image processor; wherein performing (S102, S204) the second quantization operation in the device processor to obtain the operation result comprises: performing an operation on the output data based on the weight to obtain a first operation value and obtaining a cumulative result of the first operation value and the offset value, wherein the cumulative result is fixed-point data having a second preset number of bits, and the second preset number of bits is 32; and performing the second quantization operation on the cumulative result to obtain the operation result, wherein the operation result is fixed-point data having the first preset number of bits. performing an operation on the output data based on the weight to obtain a first operation value and obtaining a cumulative result of the first operation value and the offset value, wherein the cumulative result is fixed-point data having a second preset number of bits, and the second preset number of bits is 32; and performing the second quantization operation on the cumulative result to obtain the operation result, wherein the operation result is fixed-point data having the first preset number of bits.