The invention discloses a method for accelerating an LSTM neural network
algorithm on an FPGA platform. The FPGA is a field-programmable
gate array platform and comprises a general processor, a field-programmable
gate array body and a storage module. The method comprises the following steps that an LSTM neural network is constructed by using a Tensorflow pair, and parameters of the neural networkare trained; the parameters of the LSTM network are compressed by adopting a compression means, and the problem that storage resources of the FPGA are insufficient is solved; according to the prediction process of the compressed LSTM network, a calculation part suitable for running on the field-programmable
gate array platform is determined; according to the determined calculation part, a softwareand hardware collaborative calculation mode is determined; according to the calculation logic resource and bandwidth condition of the FPGA, the number and type of IP core
firmware are determined, andacceleration is carried out on the field-programmable gate array platform by utilizing a hardware operation unit. A hardware
processing unit for acceleration of the LSTM neural network can be quicklydesigned according to hardware resources, and the
processing unit has the advantages of being high in performance and low in
power consumption compared with the general processor.