Parallel method and device for convolution computation and data loading of neural network accelerator

A parallel method for convolution computation and data loading of a neural network accelerator, comprising:

• S1:
• storing a frame of input feature maps into an input feature map cache, and dispersedly storing the input feature maps into input feature map sub-caches according to channels of the input feature maps;
• S2:
• sequentially loading a group of convolution kernels into corresponding convolution kernel cache sub-blocks in a first convolution kernel cache;
• S3:
• loading the input feature map cache and the first convolution kernel cache to execute convolution computation, putting a result into an output feature map cache, and storing a next group of convolution kernels into corresponding convolution kernel cache sub-blocks in a second convolution kernel cache, which comprises:
• S31:
• loading an input feature map instruction parameter latch, loading a convolution kernel instruction parameter latch, and under the condition that a current instruction is an input feature map loading instruction, latching an off-chip input feature map storage address and loading an input feature map length;
• and latching the number of currently loaded convolution kernels, lengths of the loaded convolution kernels, a convolution kernel cache starting address and an off-chip convolution kernel storage address under the condition that the current instruction is a convolution kernel loading instruction;
• S32:
• comparing the number of the convolution kernels, and under the condition that the total number of the convolution kernels is greater than a latch value of the number of the loaded convolution kernels and is an integer multiple, greater than 1 multiple of the number of the loaded convolution kernels in a convolution computation instruction, computing convolution and synchronously loading convolution kernels, the number of channels of the convolution kernels being the number of the loaded convolution kernels;
• under the condition that the total number of the convolution kernels is greater than the latch value of the number of the loaded convolution kernels and is 1 multiple of the number of the loaded convolution kernels in the convolution computation instruction, computing convolution and synchronously loading convolution kernels, the number of channels of the convolution kernels being a difference value between the total number of the convolution kernels and the latch value of the number of the loaded convolution kernels;
• and under the condition that the total number of the convolution kernels is equal to the latch value of the number of the loaded convolution kernels in the convolution computation instruction, determining whether to load convolution kernels in the next layer according to setting in the convolution computation instruction;
• and
• S33:
• setting a loading flag according to a comparison result of the number of the convolution kernels, the loading lag representing that data is specifically loaded into which convolution kernel cache, and setting a convolution computation starting flag;
• S34:
• synchronously carrying out convolution computation and data loading which are independent of each other;
• and
• S35:
• computing the number of remaining convolution kernels after convolution computation and data loading are completed, latching data loading parameters of the convolution kernels in S34, and returning to S32 to continue being executed until convolution computation is completed;
• S4:
• after convolution computation of the layer is completed, interchanging the input feature map cache and the output feature map cache, and using a convolution kernel cache storing an effective weight as the first convolution kernel cache to execute S3, wherein the effective weight is stored into the convolution kernel cache and is directly given by means of fields in the convolution computation instruction in S4;
• and
• S5:
• determining that all convolution computation is completed.