Procyon AI Computer Vision Benchmark : A benchmarking tool for evaluating the performance of AI inference engines on Windows PCs or Apple Macs.

Procyon AI Computer Vision Benchmark

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Overview :

The Procyon AI Computer Vision Benchmark is a specialized benchmarking tool developed by UL Solutions, designed to assist users in assessing the performance of various AI inference engines on Windows PCs or Apple Macs. This tool conducts a series of tests based on common machine vision tasks using multiple advanced neural network models, providing engineering teams with independent and standardized evaluation methods to understand the implementation quality of AI inference engines and the performance of dedicated hardware. The product supports several mainstream AI inference engines, including NVIDIA? TensorRT? and Intel? OpenVINO?, and allows comparison of the performance of floating-point and integer-optimized models. Key features include ease of installation and operation, no complex configuration required, and the ability to export detailed result files. The product is targeted at professional users, such as hardware manufacturers, software developers, and researchers, to facilitate their R&D and optimization efforts in the AI field.

Target Users :

This product is primarily designed for professional users such as engineering teams, hardware manufacturers, software developers, and researchers. They require an independent and standardized tool to evaluate the performance of AI inference engines across different hardware platforms, thereby providing data support for product development, optimization, and selection. For instance, hardware manufacturers can use this tool to test and optimize the performance of their AI accelerator hardware; software developers can assess the strengths and weaknesses of different inference engines to select the most suitable engine for their AI applications; researchers can leverage this tool to conduct performance-related AI studies.

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Use Cases

A hardware manufacturer uses this tool to test and optimize the performance of its newly released AI accelerator card. By comparing the performance of different inference engines on this hardware, they adjusted driver parameters, resulting in a significant enhancement of the accelerator card's inference performance, thereby increasing its market competitiveness.

A software development company plans to create an AI-based image recognition application. They utilized the Procyon AI Computer Vision Benchmark to test various inference engines' performance on the target hardware platform and chose the most suitable engine for integration based on the test results, ensuring efficient operation of the application.

Researchers conducting AI model optimization studies used this tool to compare the performance differences between floating-point and integer-optimized models across different hardware configurations, providing empirical evidence for choosing optimization strategies, thereby advancing relevant research.

Features

Testing using state-of-the-art neural networks based on common machine vision tasks

Measuring inference performance using CPU, GPU, or dedicated AI accelerators

Benchmarking multiple AI inference engines including NVIDIA? TensorRT? and Intel? OpenVINO?

Verifying implementation and compatibility of inference engines

Optimizing drivers for hardware accelerators

Comparing the performance of floating-point and integer-optimized models

Simple setup and use through the Procyon application or command line

How to Use

1. Visit https://benchmarks.ul.com/procyon/ai-inference-benchmark-for-windows to download the Procyon AI Computer Vision Benchmark software.

2. Install the software on your Windows PC or Apple Mac.

3. Launch the software and select the AI inference engine you wish to test.

4. Choose the neural network model for testing, such as MobileNet V3 or Inception V4, as needed.

5. Run the benchmark; the software will automatically execute a series of machine vision tasks and record performance data.

6. After testing is complete, review the generated benchmark scores, detailed scores, and hardware monitoring data to analyze the performance of different engines and models.

7. For further analysis, you can export detailed result files for research.