Power Optimization and Prediction Techniques for FPGAs - Jason H ...

1.4 Thesis Organizationto generic parameters, such as signal fanout and bounding box perimeter length. Wealso demonstrate that there is an inherent variability (noise) in switching activity andcapacitance that limits the accuracy attainable in prediction. Experimental results showthat the proposed prediction models work well given the noise limitations. This work hasbeen published in [Ande 03], [Ande 04b], and [Ande 04e].1.4 Thesis OrganizationThe remainder of this dissertation is organized as follows: Chapter 2 reviews the backgroundmaterial relevant to the research, including a description of static and dynamic power consumptionin CMOS circuits, the impact of technology scaling on leakage, an overview of FPGAtechnology, and coverage of recent research on FPGA power optimization.The main research contributions, highlighted above, are presented in Chapters 3, 4, 5, and 6.For clarity, and owing to the range of topics considered, each chapter is self-contained. Thatis, the experimental results for each proposed power optimization or prediction technique arepresented together with the technique’s description in a single chapter.Chapter 7 presents concluding remarks and suggestions for future work.7