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A semi-dynamic resource management framework for multicore embedded systems with energy harvesting




Xiang, Yi, author
Pasricha, Sudeep, advisor
Jayasumana, Anura, committee member
Siegel, H. J., committee member
Strout, Michelle Mills, committee member

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Semiconductor technology has been evolving rapidly over the past several decades, introducing a new breed of embedded systems that are tiny, efficient, and pervasive. These embedded systems are the backbone of the ubiquitous and pervasive computing revolution, embedded intelligence all around us. Often, such embedded intelligence for pervasive computing must be deployed at remote locations, for purposes of environment sensing, data processing, information transmission, etc. Compared to current mobile devices, which are mostly supported by rechargeable and exchangeable batteries, emerging embedded systems for pervasive computing favor a self-sustainable energy supply, as their remote and mass deployment makes it impractical to change or charge their batteries. The ability to sustain systems by scavenging energy from ambient sources is called energy harvesting, which is gaining monument for its potential to enable energy autonomy in the era of pervasive computing. Among various energy harvesting techniques, solar energy harvesting has attracted the most attention due to its high power density and availability. Another impact of semiconductor technology scaling into the deep submicron level is the shifting of design focus from performance to energy efficiency as power dissipation on a chip cannot increase indefinitely. Due to unacceptable power consumption at high clock rate, it is desirable for computing systems to distribute workload on multiple cores with reduced execution frequencies so that overall system energy efficiency improves while meeting performance goals. Thus it is necessary to adopt the design paradigm of multiprocessing for low-power embedded systems due to the ever-increasing demands for application performance and stringent limitations on power dissipation. In this dissertation we focus on the problem of resource management for multicore embedded systems powered by solar energy harvesting. We have conducted a substantial amount of research on this topic, which has led to the design of a semi-dynamic resource management framework designed with emphasis on efficiency and flexibility that can be applied to energy harvesting-powered systems with a variety of functionality, performance, energy, and reliability goals. The capability and flexibility of the proposed semi-dynamic framework are verified by issues we have addressed with it, including: (i) minimizing miss rate/miss penalty of systems with energy harvesting, (ii) run-time thermal control, (iii) coping with process variation induced core-to-core heterogeneity, (iv) management of hybrid energy storage, (v) scheduling of task graphs with inter-node dependencies, (vi) addressing soft errors during execution, (vii) mitigating aging effects across the chip over time, and (vii) supporting mixed-criticality scheduling on heterogeneous processors.


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embedded system
energy harvesting
low power design
scheduling algorithm


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