Predict for Deep neural networks very slow

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David Sola
David Sola 2022-11-22
编辑： David Sola 2022-11-22
Hi all together,
I have a trained LSTM and want to use it now in my productive area. What I could see is that it is quite slow in terms of execution time
I created a simple script to compare the simulation time
net = load('lstmNN.mat');
nnInput = zeros(10,55);
%%%%%
% 1 'sequenceinput' Sequence Input Sequence input with 10 dimensions
% 2 'lstm_1' LSTM LSTM with 256 hidden units
% 3 'dropout_1' Dropout 30% dropout
% 4 'lstm_2' LSTM LSTM with 128 hidden units
% 5 'dropout_2' Dropout 30% dropout
% 6 'lstm_3' LSTM LSTM with 64 hidden units
% 7 'fc' Fully Connected 2 fully connected layer
% 8 'regressionoutput' Regression Output mean-squared-error with response 'Response'
%%%%
tic
for i=1:500
predict(net.net, {nnInput});
end
toc
%Elapsed time is 22.540738 seconds.
tic
for i=1:500
net.net.predict({nnInput}, 'SequenceLength', 'shortest');
end
toc
%Elapsed time is 21.369452 seconds.
%%%% Possible usage with standard net no DNN
% tic
% for i=1:500
% net.net({nnInput});
% end
% toc
%
I can see a slight difference in the different calls but all in all it takes quite long for predicting (and I know yes the net is quite deep but nonetheless I guess the execution could be faster).
So my question would be if there is any possibility to speed up the execution time
Additionally when having a look into the profile viewer I stumbled upon the fact that the DAGNetwork.predict() function was called 1000 times (as expected) but the LSTM.forward() function was called 3000 times which actually doesn´t make sense for me (see attachement).
Thanks a lot for your support! :)

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