SELF-ADAPTIVE SELECTION AND DESIGN METHOD FOR CONVOLUTIONAL-LAYER HARDWARE ACCELERATOR

摘要

Disclosed is a self-adaptive selection and design method for a convolutional-layer hardware accelerator, comprising the following steps: (1) analyzing convolutional layer structures, designing four different hardware accelerator solutions for different kinds of convolutional layer structures, and storing the four different hardware accelerator solutions in an accelerator solution pool; and (2) obtaining a convolutional layer structure and a convolutional layer parameter from an input source, selecting, according to the convolutional layer structure, an optimal accelerator solution from the accelerator solution pool, and constructing a corresponding convolutional-layer accelerator on the basis of the optimal accelerator solution. The invention is employed to design a solution pool of convolution-layer accelerators, self-adaptively select an optimal solution, and generate a hardware accelerator, thereby enabling more flexible hardware design, while also reducing resource consumption and increasing parallel operation speeds of convolution layers.