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Modern deep neural networks increasingly make use of features such as dynamiccontrol flow, data structures and dynamic tensor shapes. Existing deep learningsystems focus on optimizing and executing static neural networks which assume apre-determined model architecture and input data shapes--assumptions which areviolated by dynamic neural networks. Therefore, executing dynamic models withdeep learning systems is currently both inflexible and sub-optimal, if notimpossible. Optimizing dynamic neural networks is more challenging than staticneural networks; optimizations must consider all possible execution paths andtensor shapes. This paper proposes Nimble, a high-performance and flexiblesystem to optimize, compile, and execute dynamic neural networks on multipleplatforms. Nimble handles model dynamism by introducing a dynamic type system,a set of dynamism-oriented optimizations, and a light-weight virtual machineruntime. Our evaluation demonstrates that Nimble outperforms state-of-the-artdeep learning frameworks and runtime systems for dynamic neural networks by upto 20x on hardware platforms including Intel CPUs, ARM CPUs, and Nvidia GPUs.

Nimble: Efficiently Compiling Dynamic Neural Networks for Model Inference