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Procyon AI Inference Benchmark For Android
Overview :
Procyon AI Inference Benchmark for Android is a benchmarking tool based on NNAPI, designed to measure AI performance and quality on Android devices. It executes common machine vision tasks through a series of popular, state-of-the-art neural network models, enabling engineering teams to independently and standardize their evaluation of NNAPI implementations and the AI performance of dedicated mobile hardware. This tool not only measures the performance of dedicated AI processing hardware on Android devices but also validates the quality of NNAPI implementations, which is significant for optimizing hardware accelerator drivers and comparing the performance of floating-point and integer optimized models.
Target Users :
The target audience includes engineering teams, especially professionals who require independent, standardized tools to assess NNAPI implementations and the AI performance of dedicated mobile hardware. This product is well-suited for them as it provides a standardized platform to test and compare the performance of different devices and models, aiding in the optimization of product performance and quality.
Use Cases
Use Procyon AI Inference Benchmark for Android to test the AI processing capabilities of new Android smartphones.
Compare the performance and accuracy of NNAPI implementations across different Android devices.
Evaluate and optimize the performance of AI applications deployed on Android devices.
Features
Testing based on common machine vision tasks using state-of-the-art neural network models.
Measuring inference performance and output quality.
Comparing performance between NNAPI, CPU, and GPU.
Validating NNAPI implementations and compatibility.
Optimizing hardware accelerator drivers.
Comparing the performance of floating-point and integer optimized models.
User-friendly, supporting direct device operation or ADB execution.
How to Use
1. Visit the official website of Procyon AI Inference Benchmark for Android.
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2. Download and install the benchmarking tool.
3. Configure the testing environment and parameters according to the user guide.
4. Run benchmark tests, including performance evaluations for NNAPI, CPU, and GPU.
5. Review the test results and performance scores, analyzing the performance of different models and hardware.
6. Optimize the performance of hardware accelerator drivers and AI applications based on the test results.
7. Utilize benchmark results for performance comparisons between devices and models.
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