# Sequential Characteristics Based Operators Disassembly Quantization Method for LSTM Layers
## 4 Experiments
### Comparison with the State of the Art
#### 4.2.3 Image Classification  
Deep neural networks (NN) can be categorized into convolution-based NN (ConvNet), fully connected NN (FCNet), and recurrent NN (RNN) [36]. ConvNet is composed of convolutional and pooling layers and is good at visual feature extraction. FCNet is composed of multiple fully connected layers and is usually used for classification. RNN is composed of fully connected layers with feedback paths and gating operations and performs well in sequential data processing. ConvNet uses Alexnet or Vgg16, etc., to extract spatial features from video frames, and RNN uses LSTM or GRU, etc., to extract sequential features.  
In order to verify the advantages of the improved algorithm, this experiment adopted four test models combined with ConvNet + FCNet + RNN for the accuracy test of the classification task. CLDNN [37], LRCN [11], AlexNet-LSTM [38], and Vgg16-LSTM [11] were selected for comparison. Operators covered by the Vgg16-LSTM classification model architecture are very comprehensive. They include both regular operators such as convolution and emerging complex operators such as LSTM. The sequential images are successively extracted from the image and sequence features, and finally, the classification prediction result of Golf Swing is obtained. All the above contrast test models are trained in the  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{3}{*}{**Precision**} & \multirow{3}{*}{\
\begin{tabular}{c} **Quantization** \\ **Method** \\ \end{tabular} } & \multicolumn{4}{c}{**Penn Treebank**} \\ \cline{3-6}  & &
\begin{tabular}{c} **Quantization** \\ **Method** \\ \end{tabular} & \multicolumn{4}{c}{**Units = 300**} \\ \cline{3-6}  & & **PPW** & **Variation** & **Relative MSE** \\ \hline
32-bit & [34] & - & 89.8 & / & / \\ \hline
3-bit & [30] & Uniform & 227.3 & 137.5 & 0.404 \\
3-bit & [31] & Balanced & 9106.4 & 9016.6 & 0.745 \\
3-bit & [32] & Greedy & 99.4 & 9.6 & 0.071 \\
3-bit & [32] & Refined & 95.4 & 5.6 & 0.060 \\
3-bit & [33] & Alternating & 93.8 & 4 & 0.043 \\ / & Ours & **91.67** & **1.87** & **0.025** \\ \hline
4-bit & [30] & Uniform & 216.3 & 126.5 & 0.302 \\
3-bit & [31] & Balanced & 8539.8 & 8450 & 0.702 \\
3-bit & [32] & Greedy & 95.0 & 5.2 & 0.042 \\
3-bit & [32] & Refined & 93.1 & 3.3 & 0.030 \\
3-bit & [33] & Alternating & 91.4 & 1.6 & 0.019 \\
3-bit & / & Ours & **89.52** & **0.28** & **0.005** \\ \hline \hline \end{tabular}
\end{table}
Table 5: Test PPW and Relative MSE of larger bits on Penn Treebank.  
Caffe framework. In this paper, sequential feature-based LSTM quantization algorithm improvements are made to the latter two models. The results are shown in Table 6, where \" \" indicates that an improved sequential quantization calibration method has been added. It is evaluates the accuracy loss (Acc loss) after quantization.  
In general, since the model applied by our method is based on the accuracy of a floating-point model of 32-bit, our technique improves quantization accuracy on average over the fixed 16-bit mode at baseline. In Vgg16-LSTM, RNN takes up a small proportion (9.52%) of total computations, which means acceleration on RNN is not very noticeable compared to ConvNet and FCNet.