Title: Structurally Sparsified Backward Propagation for Faster Long Short-Term Memory Training,
Abstract: Exploiting sparsity enables hardware systems to run neural networks faster
and more energy-efficiently. However, most prior sparsity-centric optimization
techniques only accelerate the forward pass of neural networks and usually
require an even longer training process with iterative pruning and retraining.
We observe that artificially inducing sparsity in the gradients of the gates in
an LSTM cell has little impact on the training quality. Further, we can enforce
structured sparsity in the gate gradients to make the LSTM backward pass up to
45% faster than the state-of-the-art dense approach and 168% faster than the
state-of-the-art sparsifying method on modern GPUs. Though the structured
sparsifying method can impact the accuracy of a model, this performance gap can
be eliminated by mixing our sparse training method and the standard dense
training method. Experimental results show that the mixed method can achieve
comparable results in a shorter time span than using purely dense training.