From ed415779bde0dce83cb8ed3bf75e9acd9adb3a63 Mon Sep 17 00:00:00 2001
From: Mostelk <57555939+Mostelk@users.noreply.github.com>
Date: Mon, 22 May 2023 01:09:51 -0700
Subject: [PATCH] Create README.md

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+# MLPerf™ Mobile Benchmark Suite
+MLPerf Mobile Inference Benchmark is an open-source benchmark suite for measuring how fast mobile devices (e.g. phones, laptops) can run AI tasks. 
+The benchmark is supported by an [App](https://github.com/mlcommons/mobile_app_open) which currently supports Android and iOS. 
+
+Please see the [MLPerf Mobile Inference benchmark](https://proceedings.mlsys.org/paper_files/paper/2022/file/7eabe3a1649ffa2b3ff8c02ebfd5659f-Paper.pdf) paper for a detailed description of the benchmarks along with the motivation and guiding principles behind the benchmark suite.
+If you use any part of this benchmark (e.g., reference implementations, submissions, etc.), please cite the following:
+
+@article{janapa2022mlperf,
+  title={Mlperf Mobile Inference Benchmark: An industry-standard open-source machine learning benchmark for on-device {AI}},
+  author={Janapa Reddi, Vijay and Kanter, David and Mattson, Peter and Duke, Jared and Nguyen, Thai and Chukka, Ramesh and Shiring, Ken and Tan, Koan-Sin and Charlebois, Mark and Chou, William and El-Khamy, Mostafa and others},
+  journal={Proceedings of Machine Learning and Systems},
+  volume={4},
+  pages={352--369},
+  year={2022}
+}
+
+To participate in the MLPerf Mobile Benchmark or submit results, please join the [MLCommons Mobile Working Group](https://mlcommons.org/en/groups/inference-mobile/).
+
+------
+
+This repo details the suite of models currently or previously adopted by the MLPerf Mobile Benchmark. This benchmark constitutes of a set of [computer vision models](https://github.com/mlcommons/mobile_open/tree/main/vision) and [language understanding models](https://github.com/mlcommons/mobile_open/tree/main/language/bert). 
+This repo also consitutes the [guiding rules](https://github.com/mlcommons/mobile_open/tree/main/rules) for participating or official submitting results to the MLPerf Mobile Benchmark.