An apparatus for achieving an accelerator of a sparse convolutional neural network is provided. The apparatus comprises a convolution and pooling unit, a full connection unit and a control unit. Convolution parameter information and input data and intermediate calculation data are read based on control information, and weight matrix position information of a full connection layer is also read. Then a convolution and pooling operation for a first iteration number of times is performed on the input data in accordance with the convolution parameter information, and then a full connection calculation for a second iteration number of times is performed in accordance with the weight matrix position information of the full connection layer. Each input data is divided into a plurality of sub-blocks, and the convolution and pooling unit and the full connection unit perform operations on the plurality of sub-blocks in parallel, respectively.
Please help me write a proper abstract based on the patent claims. CLAIM: 1. An apparatus for achieving an accelerator of a sparse convolutional neural network, comprising: a convolution and pooling unit for performing a convolution and pooling operation, for a first iteration number of times, on input data in accordance with convolution parameter information to finally obtain an input vector of a sparse neural network, wherein each input data is divided into a plurality of sub-blocks, and the convolution and pooling unit performs the convolution and pooling operation on the plurality of sub-blocks in parallel; a full connection unit for performing a full connection calculation, for a second iteration number of times, on the input vector in accordance with weight matrix position information of a fill connection layer to finally obtain a calculation result of the sparse convolutional neural network, wherein each input vector is divided into a plurality of sub-blocks, and the full connection unit performs a full connection operation on the plurality of sub-blocks in parallel; and a control unit for determining and sending the convolution parameter information and the weight matrix position information of the full connection layer to the convolution and pooling unit and the full connection unit respectively, and controlling reading of the input vectors on respective iterative levels in the units above and their state machines. 2. The apparatus for achieving an accelerator of a sparse convolutional neural network according to claim 1, wherein the convolution and pooling unit further comprises: a convolution unit for performing a multiplication operation of the input data and the convolution parameter; an adder tree unit for accumulating output results of the convolution unit to complete a convolution operation; a nonlinear unit for performing a nonlinear processing on a convolution operation result; and a pooling unit for performing a pooling operation on the operation result after the nonlinear processing to obtain the input data on the next iterative level or finally obtain the input vector of the sparse neural network. 3. The apparatus for achieving an accelerator of a sparse convolutional neural network according to claim 1, wherein the full connection unit further comprises: an input vector buffer unit for buffering the input vector of the sparse neural network; a pointer information buffer unit for buffering compressed pointer information of the sparse neural network in accordance with the weight matrix position information of the full connection layer; a weight information buffer unit for buffering compressed weight information of the sparse neural network in accordance with the compressed pointer information of the sparse neural network; an arithmetic logic unit (ALU) for performing a multiplication-accumulation calculation in accordance with the compressed weight information and the input vector of the sparse neural network; an output buffer unit for buffering an intermediate calculation result and a final calculation result of the ALU; and an activation function unit for performing an activation function operation on the final calculation result in the output buffer unit to obtain the calculation result of the sparse convolutional neural network. 4. The apparatus for achieving an accelerator of a sparse convolutional neural network according to claim 2, wherein the adder tree unit further adds a bias in accordance with the convolution parameter information, in addition to accumulating output results of the convolution unit. 5. The apparatus for achieving an accelerator of a sparse convolutional neural network according to claim 3, wherein the compressed weight information of the sparse neural network comprises a position index value and a weight value, and the ALU is further configured to: perform a multiplication operation of the weight value and a corresponding element of the input vector, read data in a corresponding position in the output buffer unit in accordance with the position index value, and add the data to the result of the multiplication operation above, and write the result of the addition into the corresponding position in the output buffer unit in accordance with the position index value. 6. A method for achieving an accelerator of a sparse convolutional neural network, comprising: reading convolution parameter information and input data and intermediate calculation data based on control information, and reading weight matrix position information of a full connection layer; performing a convolution and pooling operation, for a first iteration number of times, on the input data in accordance with the convolution parameter information to finally obtain an input vector of a sparse neural network, wherein each input data is divided into a plurality of sub-blocks, and the convolution and pooling operation is performed on the plurality of sub-blocks in parallel; and performing a full connection calculation, for a second iteration number of times, on the input vector in accordance with the weight matrix position information of the full connection layer to finally obtain a calculation result of the sparse convolutional neural network, wherein each input vector is divided into a plurality of sub-blocks, and a full connection operation is performed in parallel. 7. The method for achieving an accelerator of a sparse convolutional neural network according to claim 6, wherein the step of performing a convolution and pooling operation further comprises: performing a multiplication operation of the input data and the convolution parameter; accumulating output results of the multiplication operation to complete a convolution operation; performing a nonlinear processing on a convolution operation result; and performing a pooling operation on the operation result after the nonlinear processing to obtain the input data on the next iterative level or finally obtain the input vector of the sparse neural network. 8. The method for achieving an accelerator of a sparse convolutional neural network according to claim 6, wherein the step of performing a full connection calculation further comprises: buffering the input vector of the sparse neural network; buffering compressed pointer information of the sparse neural network in accordance with the weight matrix position information of the full connection layer; buffering compressed weight information of the sparse neural network in accordance with the compressed pointer information of the sparse neural network; performing a multiplication-accumulation calculation in accordance with the compressed weight information and the input vector of the sparse neural network; buffering an intermediate calculation result and a final calculation result of the multiplication-accumulation calculation; and performing an activation function operation on the final calculation result of the multiplication-accumulation calculation to obtain the calculation result of the sparse convolutional neural network. 9. The method for achieving an accelerator of a sparse convolutional neural network according to claim 7, wherein the step of accumulating output results of the multiplication operation to complete a convolution operation further comprises: adding a bias in accordance with the convolution parameter information. 10. The method for achieving an accelerator of a sparse convolutional neural network according to claim 8, wherein the compressed weight information of the sparse neural network comprises a position index value and a weight value, and the step of performing a multiplication-accumulation calculation in accordance with the compressed weight information and the input vector of the sparse neural network further comprises: performing a multiplication operation of the weight value and a corresponding element of the input vector, reading data in a corresponding position in the buffered intermediate calculation result in accordance with the position index value, and adding the data to the result of the multiplication operation above, and writing the result of the addition into the corresponding position in the buffered intermediate calculation result in accordance with the position index value.