I-Corps: Embedded Artificial Intelligence and Machine Learning for Drone Vision
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The broader impact/commercial potential of this I-Corps project is that it will enable artificial intelligence and machine learning (AI/ML) solutions to be implemented in embedded and remote devices. It provides the means to make any camera, remote sensor, or detector truly intelligent and capable of discernment and prediction; any device that has already been electronically equipped will have the potential to utilize decision making intellect. It will be a catalyst in making AI/ML truly ubiquitous and a commodity of the human experience. Intelligent sensors will help make the world safer, cameras will be able to detect anomalies and alert authorities before tragedies, and detectors will be able to predict emergencies instead of simply telemetering data. Economically, through human assistance, embedded AI/ML will enable society to be more efficient and produce greater yield. This has the potential to be a transformative technology that impacts multiple levels of society.This I-Corps project further develops an implementation of artificial intelligence and machine learning (AI/ML) directly as a hardware circuit. AI/ML have revolutionized several technical industries such as voice recognition, image processing, healthcare, and personalized advertisements. AI/ML operates by training a mathematical model, often a combination of non-linear functions, to make inferences about the model''s environment. However, AI/ML has been primarily restricted to cloud-based applications with large quantities of computing resources. In contrast, this technology implements the AI/ML model as hardware circuit that fits within a single integrated circuit. This project builds off previous technical accomplishments that implemented software design principles, such as extensibility, service-oriented architecture, modularity, and loose coupling, in hardware design through software assisted compilation. Creating a software aid to automatically restructure the hardware design enabled the hardware to change much more dynamically according to a series of parameters. By applying similar techniques to AI/ML, the technology will be able deconstruct and analyze the AI/ML model, and through a series of algorithms construct a matching hardware circuit.This award reflects NSF''s statutory mission and has been deemed worthy of support through evaluation using the Foundation''s intellectual merit and broader impacts review criteria.