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Top AI Repos tracks AI repositories on GitHub and answers two different questions about each one: is it moving right now, and would you bet a product on it.
Top AI Repos tracks AI repositories on GitHub and answers two different questions about each one: is it moving right now, and would you bet a product on it.
Benchmarking Neural Network Inference on Mobile Devices
| Date | Stars |
|---|---|
| 2026-07-31 | 388 |
| 2026-08-05 | 388 |
| 2026-08-06 | 388 |
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<div align="center"> <img src="logo.png" width="400" alt="Mobile AI Bench" /> </div> [](LICENSE) [](https://gitlab.com/llhe/mobile-ai-bench/pipelines) [FAQ](#FAQ) | [中文](README_zh.md) In recent years, the on-device deep learning applications are getting more and more popular on mobile phones or IoT devices. It's a challenging task for the developers to deploy their deep learning models in their mobile applications or IoT devices. They need to optionally choose a cost-effective hardware solution (i.e. chips and boards), then a proper inference framework, optionally utilizing quantization or compression techniques regarding the precision-performance trade-off, and finally run the model on one or more of heterogeneous computing devices. How to make an appropriate decision among these choices is a tedious and time-consuming task. **Mobile AI Benchmark** (i.e. **MobileAIBench**) is an end-to-end benchmark tool which covers different chips and inference frameworks, with results include both speed and model accuracy, which will give insights for developers. ## Daily Benchmark Results Please check *benchmark* step in [daily CI pipeline page](https://gitlab.com/llhe/mobile-ai-bench/pipelines), due to the lack of test devices, the CI result may not cover all hardwares and frameworks. ## FAQ **Q: Why are benchmark results not stable on my device?** **A**: Due to power save considerations, some SoCs have aggressive and advanced power control scheduling to reduce power consumption which make performance quite unstable (especially CPU). Benchmark results highly depend on states of devices, e.g., running processes, temperature, power control policy. It is recommended to disable power control policy (as shown in `tools/power.sh`) if possible (e.g., rooted phone). Otherwise, keep your device at idle state with low temperature, and benchmark one model on one framework each time. **Q: Why do some devices run faster (or slower) than expected in the CI benchmark result?** **A**: Some devices is rooted and has some specialized performance tuning while some others is not rooted and failed to make such tuning (see the code for more details). **Q: Why is ncnn initialization time much less than others?** **A**: ncnn benchmark uses fake model parameters and skips loading weights from filesystem. **Q: Does benchmark use all available cores of devices?** **A**: Most modern Android phones use [ARM big.LITTLE](https://en.wikipedia.org/wiki/ARM_big.LITTLE) architecture which can lead to significant variance between different runs of the benchmark, we use only available big cores to reduce this variance by `taskset` command for MACE/NCNN/TFLITE benchmark. Moreover, there are no well-defined APIs for SNPE to bind to big cores and set thread count. Thread count can be set by adding `--num_threads` to `tools/benchmark.sh` command. ## Environment requirement MobileAIBench supports several deep learning frameworks (called `executor` in this project, i.e., [MACE](https://github.com/XiaoMi/mace), [SNPE](https://developer.qualcomm.com/software/qualcomm-neural-processing-sdk), [ncnn](https://github.com/Tencent/ncnn), [TensorFlow Lite](https://github.com/tensorflow/tensorflow/tree/master/tensorflow/contrib/lite) and [HIAI](https://developer.huawei.com/consumer/en/devservice/doc/2020314)) currently, which may require the following dependencies: | Software | Installation command | Tested version | | :-------: | :-------------------: | :-------------: | | Python | | 2.7 | | ADB | apt-get install android-tools-adb | Required by Android run, >= 1.0.32 | | Android NDK | [NDK installation guide](https://developer.android.com/ndk/guides/setup#install) | Required by Android build, r15c | | Bazel | [bazel installation guide](https://docs.bazel.build/versions/master/install.html) | 0.13.0 | | CM
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Read on GitHubWould you bet a product on this? Bounded 0–100 and slow moving.
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