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Optimizing deep learning models is generally performed in two steps: (i)high-level graph optimizations such as kernel fusion and (ii) low level kerneloptimizations such as those found in vendor libraries. This approach oftenleaves significant performance on the table, especially for the case ofrecursive deep learning models. In this paper, we present Cortex, acompiler-based approach to generate highly-efficient code for recursive modelsfor low latency inference. Our compiler approach and low reliance on vendorlibraries enables us to perform end-to-end optimizations, leading to up to 14Xlower inference latencies over past work, across different backends.

Cortex: A Compiler for Recursive Deep Learning Models