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Embedded Computing for High Performance

Efficient Mapping of Computations Using Customization, Code Transformations and Compilation

Autor: Jo?o Cardoso, José Gabriel F. Coutinho, Pedro C. Diniz

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Editura: Elsevier Science & Technology

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Embedded Computing for High Performance: Design Exploration and Customization using High-level Compi...

Informații despre carte

Autor

Jo?o Cardoso, José Gabriel F. Coutinho, Pedro C. Diniz

Limbă

engleză

Legare

Carte - Carte broșată

Publicat

2017

Pagini

320

EAN

9780128041895

ISBN

0128041897

Enbook ID

14530580

Editura

Elsevier Science & Technology

Greutate

664

Dimensiuni

234 x 192 x 18

Descriere completă

Embedded Computing for High Performance: Design Exploration and Customization using High-level Compilation and Synthesis Tools provides a set of real-life example implementations that migrate traditional desktop systems to embedded systems. Working with popular hardware, including Xilinx and ARM, the book offers a comprehensive description of techniques for mapping computations expressed in programming languages such as C or MATLAB to high-performance embedded architectures consisting of multiple CPUs, GPUs, and reconfigurable hardware (FPGAs). The authors demonstrate a domain-specific language (LARA) that facilitates retargeting to multiple computing systems using the same source code. In this way, users can decouple original application code from transformed code and enhance productivity and program portability. After reading this book, engineers will understand the processes, methodologies, and best practices needed for the development of applications for high-performance embedded computing systems. Focuses on maximizing performance while managing energy consumption in embedded systemsExplains how to retarget code for heterogeneous systems with GPUs and FPGAsDemonstrates a domain-specific language that facilitates migrating and retargeting existing applications to modern systemsIncludes downloadable slides, tools, and tutorials

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Embedded Computing for High Performance

Efficient Mapping of Computations Using Customization, Code Transformations and Compilation

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