Aiming at the problem of high computational overhead and redundancy in LSTM model

this paper proposed a design method of LSTM accelerator based on data sparsity. This scheme uses Delta network algorithm to mine the sparsity of input data

and filters the multiplication of redundant matrix vectors without irregular loading of data. The calculation mode of matrix-vector multiplication is modeled to find the most efficient parallel array computing architecture design scheme. The experimental results on MINIST dataset show that when the filter threshold of Delta network algorithm is less than 0.5

the detection accuracy of LSTM neural network algorithm remains unchanged

and the computational performance is improved by 21.53 times.