The invention belongs to the technical field of computer
system structure design, and particularly relates to a cloud deep neural network optimization method based on CPU and FPGA cooperative computing. The method is divided into a front end part and a rear end part. The front end is a
server taking a CPU as a core and is responsible for flow control, data receiving and partial
processing; and therear end is an acceleration component taking the FPGA as a core, comprises a large-scale parallel
processor array, a graphic
processing unit, an application-specific
integrated circuit and a PCI-E interface, and is responsible for parallel acceleration
processing and the like of a key layer of the deep neural network. Firstly, the deep neural network is divided into two parts suitable for front-end processing and rear-end processing according to different levels; the front end shuttles the received data between the front end and the rear end by DDR in the form of a
data stream to process eachlayer or a combined layer. The front-end flexible
process control is matched with the rear-end efficient parallel structure, so that the energy efficiency ratio of neural network calculation can be greatly improved.