AI vision edge computing SOC test fixture

In the field of AI vision edge computing, the mainstream SoC (System-on-Chip) testing fixtures generally include the following types:

1. Test board: Used to verify the functionality and performance of the SoC. Test boards typically include the SoC chip, memory, interfaces, etc., and can comprehensively test the SoC through various sensors, cameras, and other peripherals.
2. Video testing module: Responsible for verifying the SoC’s video processing capabilities, including video encoding/decoding, image processing, etc. This module usually includes video input/output interfaces and related testing circuits.
3. Power management testing module: Used to test the SoC’s power management functions, including power consumption, temperature, etc.
4. Interface testing module: Used to verify the functionality and performance of various interfaces of the SoC, including USB, HDMI, Ethernet, etc.
5. Automated testing system: Combines software and hardware to automatically execute test cases, improving testing efficiency and accuracy.

These testing fixtures are typically designed and manufactured by specialized testing equipment manufacturers, and there may be customized requirements for different SoC products. As AI vision edge computing technology advances, testing fixtures are also evolving to meet new functional and performance testing requirements.

During testing, the testing fixtures for AI vision edge computing SoCs need to meet the following criteria:

1. Accuracy: The testing fixtures need to accurately simulate real-world working conditions to ensure the reliability and accuracy of test results.
2. Comprehensiveness: The testing fixtures need to cover all functions and performance aspects of the SoC, including AI model inference, image processing, video encoding/decoding, etc.
3. Scalability: The testing fixtures should have a certain degree of scalability to adapt to different models and configurations of SoC products.
4. Flexibility: The testing fixtures need to have a certain degree of flexibility to adapt to different testing scenarios and requirements, supporting quick switching of test cases.
5. Efficiency: The testing fixtures should have efficient testing speed and automation level to improve testing efficiency and reduce testing costs.
6. Stability: The testing fixtures need to have good stability and reliability to run stably for long periods and ensure consistent test results.
7. Usability: The operation interface of the testing fixtures should be simple and intuitive, easy to operate and manage, reducing the possibility of human errors.
8. Traceability: The testing fixtures need to record key parameters and data during the testing process for subsequent analysis and traceability of test results.

In summary, testing fixtures need to meet criteria such as accuracy, comprehensiveness, scalability, flexibility, efficiency, stability, usability, and traceability during the testing process to ensure comprehensive testing and verification of AI vision edge computing SoCs.