AI chip evaluation parameter determination method based on deep residual neural network

The invention discloses an AI chip evaluation parameter determination method based on a deep residual neural network, and the method comprises the steps: designing a deep residual neural network FDNet, training the deep residual neural network FDNet to obtain a floating point model, and obtaining an evaluation parameter under a target classification task; quantifying the floating point model to obtain a fixed point model, deploying the fixed point model on a hardware board card, and obtaining evaluation parameters under the target classification task through reasoning on the hardware board card; evaluation parameters obtained by the obtained floating point model and the fixed point model are used for evaluating the application performance of the AI chip when the deep residual neural network FDNet is used. The method can be suitable for various aerospace data sets and is high in robustness. 本发明公开了一种基于深层残差神经网络的AI芯片测评参数确定方法，包括：设计深层残差神经网络FDNet，对该深层残差神经网络FDNet进行训练得到浮点模型，并得到目标分类任务下的测评参数；将浮点模型进行量化得到定点模型，将定点模型部署于硬件板卡