Scalable and Near-Optimal Design Space Exploration for Embedded Systems electronic resource by Angeliki Kritikakou, Francky Catthoor, Costas Goutis.
Material type: TextPublication details: Cham : Springer International Publishing : Imprint: Springer, 2014Description: XVII, 277 p. 80 illus., 2 illus. in color. online resourceContent type: text Media type: computer Carrier type: online resourceISBN: 9783319049427Subject(s): engineering | Computer Science | electronics | Systems engineering | Engineering | Circuits and Systems | Processor Architectures | Electronics and Microelectronics, Instrumentation | Energy, generalDDC classification: 621.3815 LOC classification: TK7888.4Online resources: Click here to access onlineIntroduction & 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. • 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 optimization step for both regular and irregular memory accesses.
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