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TIRAMISU: A Polyhedral Compiler for Dense and Sparse Deep Learning
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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).
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Cited by 1 Pith paper
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Pearl: Automatic Code Optimization Using Deep Reinforcement Learning
An RL agent with a graph neural network learns loop nest optimizations for the Tiramisu compiler and generalizes to unseen benchmarks, reporting 2.02x and 3.36x geometric mean speedups over Tiramisu and Pluto.
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