Scalable and Near-Optimal Design Space Exploration for Embedded Systems [Paperback]

This book describes scalable and near-optimal, processor-level design space exploration (DSE) methodologies. The authors present design methodologies for data storage and processing in real-time, cost-sensitive data-dominated embedded systems. Readers will be enabled to reduce time-to-market, while satisfying system requirements for performance, area, and energy consumption, thereby minimizing the overall cost of the final design.Introduction & Motivation.- Reusable DSE methodology for scalable & near-optimal frameworks.- Part I Background memory management methodologies.- Development of intra-signal in-place methodology.- Pattern representation.- Intra-signal in-place methodology for non-overlapping scenario.- Intra-signal in-place methodology for overlapping scenario.- Part II Processing related mapping methodologies.- Design-time scheduling techniques DSE framework.- Methodology to develop design-time scheduling techniques under constraints.- Design Exploration Methodology for Microprocessor & HW accelerators.- Conclusions & Future Directions.

This book describes scalable and near-optimal, processor-level design space exploration (DSE) methodologies.? The authors present design methodologies for data storage and processing in real-time, cost-sensitive data-dominated embedded systems.? Readers will be enabled to reduce time-to-market, while satisfying system requirements for performance, area, and energy consumption, thereby minimizing the overall cost of the final design.
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?Describes design space exploration (DSE) methodologies for data storage and processing in embedded systems, which achieve near-optimal solutions with scalable exploration time;
?Presents a set of principles and the processes which support the development of the proposed scalable and near-optimal methodologies;
?Enables readers to apply scalable and near-optimal methodologies to the intra-signal in-place optimizatil³Q