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Taking the implementation of FY coal power ERP project as the case, this paper analyzes in detail the methods and approaches of risk assessment and control in the implementation of ERP project in coal enterprises[1]. The group already has a certain foundation in the construction of information technology, but it has objectively many problems in various management, which determines that it has an urgent need for ERP system, and ERP system is often accompanied by some risks[2]. To reduce the probability of occurrence of risk, it is necessary to evaluate the risk. This paper mainly uses AHP to conduct risk assessment of the project [3].

Deep neural network (DNN) has become a pivotal machine learning and object recognition technology in the big data era. The softmax layer is one of the key component layers for completing multi-classification tasks. However, the softmax layer contains complex exponential and division operations, resulting in low accuracy and long critical paths in hardware accelerator design. In order to solve the above issues, we present a softmax hardware architecture with proper accuracy, good trade-off and strong expansibility. We summarize the classification rules of neural network and balance the calculation accuracy between resource consumption. On this basis, we proposed an exponential calculation unit based on the group lookup table, and improve a natural logarithmic calculation unit based on the Maclaurin series and the data preprocessing scheme matching them. The experimental results show that the softmax hardware architecture proposed in this paper can achieve the calculation accuracy of 3 decimal fraction and the classification accuracy of $99.01%$. Theoretically, it can accomplish the classification task of infinite categories.