New Paradigm in Robust Infrastructure Scalability for Autonomous Applications. 2019-01-0495
Artificial Intelligence (A.I.) and Big Data are increasing become more applicable in the development of technology from machine design and mobility to bio-printing and drug discovery. The ability to quantify large amounts of data these systems generate will be paramount to establishing a robust infrastructure for interdisciplinary applications. This paper purposes a new paradigm for the environment, pre/post data processing, integration, and system security for robust systems in mobility. The systems integration is based on a novel FPGA embedded system design and computing (EDGE) platform utilizing image processing c-NN algorithms from High Energy Physics (HEP) experiments with associative memory to ROS- FPGA technology for hyper-scale infrastructure scalability in autonomous applications. The ability to process data on this scale is equivalent to collision particle detection that LHC produces at CERN. The future of robust scalability will depend upon how seamlessly a number of applications can be integrated into a high performance package with low consumption. The proposed architecture will entirely be dependent on a digital network with special attention paid to costs and power consumption needed for a server-less platform. In Sect. II, a background of autonomy, industry analysis, current level of autonomous, trucking industry needs, and the automotive industry race for driverless vehicles are provided. Sect. III, outlines the current challenges of autonomous and ability to quantify large amounts of data. Sect. IV, proposes solutions for a radical new paradigm addressing both macro and micro scalability. Lastly, Sect. V., provides logic gate analysis, compilation, and simulation for associative memory design.