CAREER: D3: Addressing Emerging Data-Induced Challenges in Embedded and Real-Time Systems

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Data-driven embedded systems are here. The ability to create algorithms that process massive amounts of real-time sensor-captured data is enabling designers in many industries to develop intelligent embedded systems that automate actions and decisions, e.g., self-driving vehicles. This emerging data-intensive embedded computing paradigm brings a new set of data-induced challenges around guaranteeing timing predictability and enabling latency constraints to be analytically validated at design time. The goal of this research is to overcome challenges due to real-time processing of massive data in embedded systems in order to guarantee timing predictability. D3, a comprehensive resource management ecosystem with the capability of predictably processing massive real-time data-intensive workloads, is implemented in the operating system. D3 brings in a novel set of fundamental system-level techniques, which enable smart data filtering and characterization, transparent and supervised streaming for execution concurrency optimization, and predictable memory management under heterogeneous architectures. The hard algorithmic challenges due to co-scheduling memory and highly heterogeneous computing resources such that analytical guarantees on timing predictability become quantifiable will be addressed. Developing a comprehensive heterogeneous resource management ecosystem for predictably processing massive real-time data-intensive workloads would be a significant result for many application domains, such as transportation and robotics. The next-generation automotive system is a good example that could greatly benefit from the proposed research. The outcome of this project will pave the way to certifiability of safety-critical autonomous vehicles based on heterogeneous platforms. An innovative undergraduate research project to develop an educational tool that uses real-time data-driven system design concepts as its foundation assists in realizing the educational objectives. All source code and evaluation data will be freely available for direct download from public web servers maintained by the University of Texas at Dallas (UTD) Computer Science Department under an open source Gnu Public License. Additional case-study test programs and scripts will be freely available under the open source BSD license and directly downloadable from public web servers maintained by the UTD Computer Science Department. All data products produced in this project will also be archived in the UTD computer science computing repository for permanent storage. The repository is available at www.utdallas.edu/~cong/CareerRepo.