  Neural network pruning and quantization techniques are almost as old as
neural networks themselves. However, to date only ad-hoc comparisons between
the two have been published. In this paper, we set out to answer the question
on which is better: neural network quantization or pruning? By answering this
question, we hope to inform design decisions made on neural network hardware
going forward. We provide an extensive comparison between the two techniques
for compressing deep neural networks. First, we give an analytical comparison
of expected quantization and pruning error for general data distributions.
Then, we provide lower bounds for the per-layer pruning and quantization error
in trained networks, and compare these to empirical error after optimization.
Finally, we provide an extensive experimental comparison for training 8
large-scale models on 3 tasks. Our results show that in most cases quantization
outperforms pruning. Only in some scenarios with very high compression ratio,
pruning might be beneficial from an accuracy standpoint.
