Research on computing power test of car-level chips based on neural network algorithm

Lili Lei; Chunlei Ma

doi:10.1117/12.3025462

28 February 2024 Research on computing power test of car-level chips based on neural network algorithm

Lili Lei, Chunlei Ma

Proceedings Volume 13071, International Conference on Mechatronic Engineering and Artificial Intelligence (MEAI 2023); 130712M (2024) https://doi.org/10.1117/12.3025462
Event: International Conference on Mechatronic Engineering and Artificial Intelligence (MEAI 2023), 2023, Shenyang, China

Abstract

With the rapid development of intelligence, the function of the whole vehicle is becoming more and more complex. More and more cameras, millimeter-wave radars, laser radars, ultrasonic sensors, and various other data acquisition devices are equipped on vehicles to support the evolution of autonomous driving capabilities. Compared with the wave of computing chip research and development in full swing, the development of computing chip testing and certification capabilities has seriously lagged behind. In this paper, a set of computing power test system is built based on TDA4VM development board. This method measures the actual computing power of the chip under the premise that the AI computing unit of the computing chip is squeezed to the maximum extent. It provides a new method for the calculation force test of the car-level computing chip. It forms an independent and controllable test and evaluation ability of the vehicle computing chip, and supports the chip selection of the car manufacturers.

(2024) Published by SPIE. Downloading of the abstract is permitted for personal use only.

Citation Download Citation

Lili Lei and Chunlei Ma "Research on computing power test of car-level chips based on neural network algorithm", Proc. SPIE 13071, International Conference on Mechatronic Engineering and Artificial Intelligence (MEAI 2023), 130712M (28 February 2024); https://doi.org/10.1117/12.3025462

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KEYWORDS

Artificial intelligence

Evolutionary algorithms

Detection and tracking algorithms

Education and training

Neural networks

Oscilloscopes

Field programmable gate arrays

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