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Found 3 papers, 2 papers with code
TorchBench: Benchmarking PyTorch with High API Surface Coverage
TorchBench is able to comprehensively characterize the performance of the PyTorch software stack, guiding the performance optimization across models, PyTorch framework, and GPU libraries.
Using Python for Model Inference in Deep Learning
We evaluate our design on a suite of popular PyTorch models on Github, showing how they can be packaged in our inference format, and comparing their performance to TorchScript.
Intel nGraph: An Intermediate Representation, Compiler, and Executor for Deep Learning
The current approach, which we call "direct optimization", requires deep changes within each framework to improve the training performance for each hardware backend (CPUs, GPUs, FPGAs, ASICs) and requires $\mathcal{O}(fp)$ effort; where $f$ is the number of frameworks and $p$ is the number of platforms.
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