Recurrent Neural Networks (RNNs) are used in state-of-the-art models in
domains such as speech recognition, machine translation, and language
modelling. Sparsity is a technique to reduce compute and memory requirements of
deep learning models. Sparse RNNs are easier to deploy on devices and high-end
server processors. Even though sparse operations need less compute and memory
relative to their dense counterparts, the speed-up observed by using sparse
operations is less than expected on different hardware platforms. In order to
address this issue, we investigate two different approaches to induce block
sparsity in RNNs: pruning blocks of weights in a layer and using group lasso
regularization to create blocks of weights with zeros. Using these techniques,
we demonstrate that we can create block-sparse RNNs with sparsity ranging from
80% to 90% with small loss in accuracy. This allows us to reduce the model size
by roughly 10x. Additionally, we can prune a larger dense network to recover
this loss in accuracy while maintaining high block sparsity and reducing the
overall parameter count. Our technique works with a variety of block sizes up
to 32x32. Block-sparse RNNs eliminate overheads related to data storage and
irregular memory accesses while increasing hardware efficiency compared to
unstructured sparsity.

研究使用 “修剪” 和 “组稀疏化正则化” 这两种方法来诱导 RNN 网络中的块稀疏性，形成 80% 到 90% 不等的稀疏度；该技术能够消除与数据存储和不规则内存访问相关的额外开销，同时提高硬件效率和降低参数总数。