  In this paper, we demonstrate a compiler that can optimize sparse and
recurrent neural networks, both of which are currently outside of the scope of
existing neural network compilers (sparse neural networks here stand for
networks that can be accelerated with sparse tensor algebra techniques). Our
demonstration includes a mapping of sparse and recurrent neural networks to the
polyhedral model along with an implementation of our approach in TIRAMISU, our
state-of-the-art polyhedral compiler. We evaluate our approach on a set of deep
learning benchmarks and compare our results with hand-optimized industrial
libraries. Our results show that our approach at least matches Intel MKL-DNN
and in some cases outperforms it by 5x (on multicore-CPUs).
