Simulation implementation method, neural network compiler and computer readable storage medium

Abstract

The invention discloses a simulation implementation method, a neural network compiler and a computer readable storage medium, and relates to the technical field of deep learning. The simulation implementation method comprises the following steps that: a quantization set picture quantifies a neural network model through the neural network compiler to generate an executable file, a ten-thousand-person test set generates first input data, a first fixed-point feature file and a floating-point feature file through a neural network compiler, and if a statistical result of a precision table conforms to a preset precision range, the executable file and the first input data are read for simulation of a neural network model. The invention has the beneficial effects that batch simulation of a plurality of different types of neural network models is realized, the correctness of transplantation to a chip or an FPGA is ensured, simulation is performed layer by layer for different types of neural network models, more simulation verification points are covered, the risk of chip tape-out is prevented, and meanwhile, comprehensive precision verification is carried out on a precision table for counting the neural network model.

Description

technical field [0001] The present application belongs to the technical field of deep learning, and more specifically, the present application relates to a simulation implementation method, a neural network compiler, and a computer-readable storage medium. Background technique [0002] With the development of Internet technology, the massive data collected provides enough scenarios for deep learning training. The development of intelligent algorithms based on convolutional neural networks relies on massive data. In the fields of image classification and object recognition, intelligent algorithms The accuracy has exceeded the recognition accuracy of humans. [0003] If the neural network algorithm wants to be implemented in the security field, it is necessary to parse the algorithm model trained on the server into a computer language recognizable by the embedded chip, so as to facilitate the installation and monitoring of security cameras. [0004] The process of implementin...

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Problems solved by technology

[0006] The purpose of this application is to provide a simulation implementation method, a neural network compiler, and a computer-readable storage medium to solve the problems in the above-mentioned prior art that can only simulate each intermediate layer of a neural network model and cannot perform a 10,000-person test set. Technical Issues of Accuracy Testing

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