With the development of the times, the objective requirement of CIM is to establish a 3D digital city. This article proposes a software front-end and back-end open source solution for the production and release of 3D modeling in different industries. This article is based on the construction practice of CIM basic platform database, and analyzes the spatial resolution and corresponding entity expression effect of 3D models in CIM basic platform from the perspective of CIM industry application, as well as the advantages and disadvantages of application. It also elaborates on the classification of 3D models and the geographic elements and buildings they contain, and introduces the relationship between 3D models and vector data, raster data, attribute data, as well as the difficulties of modeling technology. On this basis, large-scale 3D modeling and mapping experiments were conducted in the power industry, water industry, construction industry, and indoormap scene. The article has improved and optimized the algorithms for 3D excavation volume and wire azimuth, and introduced various parametric modeling techniques. From the perspective of project application and user feedback, the modeling quality, performance, and user experience have been recognized by the market. This article has certain reference value for the construction of the 3D model basic framework, theme oriented model system, modeling technology selection, and the release of massive 3D data services in the CIM basic platform construction.

The unprecedented progress in Artificial Intelligence (AI), particularly in deep learning algorithms with ubiquitous internet connected smart devices, has created a high demand for AI computing on the edge devices. This review studied commercially available edge processors, and the processors that are still in industrial research stages. We categorized state-of-the-art edge processors based on the underlying architecture, such as dataflow, neuromorphic, and Processing in-Memory (PIM) architecture. The processors are analyzed based on their performance, chip area, energy efficiency, and application domains. The supported programming frameworks, model compression, data precision, and the CMOS fabrication process technology are discussed. Currently, most of the commercial edge processors utilize dataflow architectures. However, emerging non-von Neumann computing architectures have attracted the industry in recent years. Neuromorphic processors are highly efficient for performing computation with fewer synaptic operations, and several neuromorphic processors offer online training for secured and personalized AI applications. This review found that the PIM processors show significant energy efficiency and consume less power compared to dataflow and neuromorphic processors. The future direction of the industry would be to implement state-of-the-art deep learning algorithms in emerging non-Von Neumann computing paradigms for low power computing on the edge devices.