Artificial intelligence(AI)is one of the core drivers of industrial development and a critical factor in promoting the integration of emerging technologies,such as graphic processing unit,Internet of Things,cloud comp...Artificial intelligence(AI)is one of the core drivers of industrial development and a critical factor in promoting the integration of emerging technologies,such as graphic processing unit,Internet of Things,cloud computing,and the blockchain,in the new generation of big data and Industry 4.0.In this paper,we construct an extensive survey over the period 1961-2018 of AI and deep learning.The research provides a valuable reference for researchers and practitioners through the multi-angle systematic analysis of AI,from underlying mechanisms to practical applications,from fundamental algorithms to industrial achievements,from current status to future trends.Although there exist many issues toward AI,it is undoubtful that AI has become an innovative and revolutionary assistant in a wide range of applications and fields.展开更多
In the design of a graphic processing unit(GPU),the processing speed of triangle rasterization is an important factor that determines the performance of the GPU.An architecture of a multi-tile parallel-scan rasterizat...In the design of a graphic processing unit(GPU),the processing speed of triangle rasterization is an important factor that determines the performance of the GPU.An architecture of a multi-tile parallel-scan rasterization accelerator was proposed in this paper.The accelerator uses a bounding box algorithm to improve scanning efficiency.It rasterizes multiple tiles in parallel and scans multiple lines at the same time within each tile.This highly parallel approach drastically improves the performance of rasterization.Using the 65 nm process standard cell library of Semiconductor Manufacturing International Corporation(SMIC),the accelerator can be synthesized to a maximum clock frequency of 220 MHz.An implementation on the Genesys2 field programmable gate array(FPGA)board fully verifies the functionality of the accelerator.The implementation shows a significant improvement in rendering speed and efficiency and proves its suitability for high-performance rasterization.展开更多
文摘Artificial intelligence(AI)is one of the core drivers of industrial development and a critical factor in promoting the integration of emerging technologies,such as graphic processing unit,Internet of Things,cloud computing,and the blockchain,in the new generation of big data and Industry 4.0.In this paper,we construct an extensive survey over the period 1961-2018 of AI and deep learning.The research provides a valuable reference for researchers and practitioners through the multi-angle systematic analysis of AI,from underlying mechanisms to practical applications,from fundamental algorithms to industrial achievements,from current status to future trends.Although there exist many issues toward AI,it is undoubtful that AI has become an innovative and revolutionary assistant in a wide range of applications and fields.
基金the Scientific Research Program Funded by Shaanxi Provincial Education Department(20JY058)。
文摘In the design of a graphic processing unit(GPU),the processing speed of triangle rasterization is an important factor that determines the performance of the GPU.An architecture of a multi-tile parallel-scan rasterization accelerator was proposed in this paper.The accelerator uses a bounding box algorithm to improve scanning efficiency.It rasterizes multiple tiles in parallel and scans multiple lines at the same time within each tile.This highly parallel approach drastically improves the performance of rasterization.Using the 65 nm process standard cell library of Semiconductor Manufacturing International Corporation(SMIC),the accelerator can be synthesized to a maximum clock frequency of 220 MHz.An implementation on the Genesys2 field programmable gate array(FPGA)board fully verifies the functionality of the accelerator.The implementation shows a significant improvement in rendering speed and efficiency and proves its suitability for high-performance rasterization.
