Recurrent Neural Networks (RNNs) have been widely used in sequence analysis
and modeling. However, when processing high-dimensional data, RNNs typically
require very large model sizes, thereby bringing a series of deployment
challenges. Although various prior works have been proposed to reduce the RNN
model sizes, executing RNN models in resource-restricted environments is still
a very challenging problem. In this paper, we propose to develop extremely
compact RNN models with fully decomposed hierarchical Tucker (FDHT) structure.
The HT decomposition does not only provide much higher storage cost reduction
than the other tensor decomposition approaches but also brings better accuracy
performance improvement for the compact RNN models. Meanwhile, unlike the
existing tensor decomposition-based methods that can only decompose the
input-to-hidden layer of RNNs, our proposed fully decomposition approach
enables the comprehensive compression for the entire RNN models with
maintaining very high accuracy. Our experimental results on several popular
video recognition datasets show that our proposed fully decomposed hierarchical
tucker-based LSTM (FDHT-LSTM) is extremely compact and highly efficient. To the
best of our knowledge, FDHT-LSTM, for the first time, consistently achieves
very high accuracy with only few thousand parameters (3,132 to 8,808) on
different datasets. Compared with the state-of-the-art compressed RNN models,
such as TT-LSTM, TR-LSTM and BT-LSTM, our FDHT-LSTM simultaneously enjoys both
order-of-magnitude (3,985x to 10,711x) fewer parameters and significant
accuracy improvement (0.6% to 12.7%).

本文提出了一种具有全分解层级 Tucker 结构的极度紧凑的 RNN 模型，该模型不仅提供了比其他张量分解方法更高的存储成本降低幅度，而且为整个 RNN 模型的综合压缩提供了可能，且在多个视频识别数据集上进行的实验结果表明，该模型具有高度准确性和极高的效率。