Activity Sparsity Complements Weight Sparsity for Efficient RNN Inference

TL;DR

This paper demonstrates that combining activity sparsity with weight sparsity in RNNs significantly reduces computation while maintaining high performance, advancing efficient deep learning and neuromorphic computing applications.

Contribution

It shows that activity sparsity can multiplicatively complement weight sparsity in RNNs, achieving unprecedented computational reduction without performance loss.

Findings

Up to 20x reduction in computation on Penn Treebank

Maintained perplexity below 60 with activity sparsity

First to combine activity and weight sparsity effectively in RNNs

Abstract

Artificial neural networks open up unprecedented machine learning capabilities at the cost of ever growing computational requirements. Sparsifying the parameters, often achieved through weight pruning, has been identified as a powerful technique to compress the number of model parameters and reduce the computational operations of neural networks. Yet, sparse activations, while omnipresent in both biological neural networks and deep learning systems, have not been fully utilized as a compression technique in deep learning. Moreover, the interaction between sparse activations and weight pruning is not fully understood. In this work, we demonstrate that activity sparsity can compose multiplicatively with parameter sparsity in a recurrent neural network model based on the GRU that is designed to be activity sparse. We achieve up to $20\times$ reduction of computation while maintaining perplexities below $60$ on the Penn Treebank language modeling task. This magnitude of reduction has not been achieved previously with solely sparsely connected LSTMs, and the language modeling performance of our model has not been achieved previously with any sparsely activated recurrent neural networks or spiking neural networks. Neuromorphic computing devices are especially good at taking advantage of the dynamic activity sparsity, and our results provide strong evidence that making deep learning models activity sparse and porting them to neuromorphic devices can be a viable strategy that does not compromise on task performance. Our results also drive further convergence of methods from deep learning and neuromorphic computing for efficient machine learning.