基于多类网络模型的AI芯片测评方法研究

随着AI芯片制程不断升级以及结构设计不断优化，AI芯片在并行计算能力和泛化能力等方面展现出明显的优势，这些优势使得AI芯片在宇航领域得到越来越多的关注和应用。与传统的质量保证方法不同，AI芯片测评作为一项新的重要环节，对于AI芯片的选型和使用具有重要意义。本文针对面向宇航应用的AI芯片，研究了一种宇航用AI芯片的测评方法，该方法可以测评出AI芯片的精度、算力、功耗和量化支持能力，并基于该方法对AI芯片的性能和功能性指标进行了详细的分析和评价，最后针对宇航用AI芯片测评方法的进一步研究方向提出了相关建议。

AI Chip Evaluation Based on Multi Class Network Model

With the continuous upgrading of AI chip processing and optimization of structural design, AI chips have shown significant advantages in parallel computing and generalization capabilities, which has attracted more and more attention and applications in the aerospace field. Unlike traditional quality assurance methods, AI chip evaluation, as a new and important aspect, is of great significance for the selection and use of AI chips. This article focuses on the evaluation method of AI chips for aerospace applications, and conducts a detailed analysis and evaluation of indicators such as accuracy, computing power, power consumption, and quantitative support. Finally, relevant suggestions are proposed for the evaluation method of AI chips in aerospace applications.