High-Performance Embedded Computing. Architectures, by Marilyn Wolf (Auth.)

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By Marilyn Wolf (Auth.)

High-Performance Embedded Computing, moment variation, combines modern learn with useful suggestions in a number of embedded computing themes, together with real-time platforms, computing device structure, and low-power layout. writer Marilyn Wolf provides a complete survey of the cutting-edge, and publications you to accomplish excessive degrees of functionality from the embedded platforms that deliver those applied sciences jointly. The booklet covers CPU layout, working structures, multiprocessor courses and architectures, and masses extra. Embedded computing is a key portion of cyber-physical structures, which mix actual units with computational assets for regulate and verbal exchange. This revised version provides new content material and examples of cyber-physical structures through the e-book, together with layout methodologies, scheduling, and wide-area CPS to demonstrate the chances of those new platforms. Revised and up to date with insurance of lately built customer electronics architectures and versions of computing contains new VLIW processors equivalent to the TI Da Vinci, and CPU simulation research model-based verification and middleware for embedded platforms Supplemental fabric contains lecture slides, labs, and extra assets

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Data parallelism on a small scale can be found in a basic block of a program. Largerscale data parallelism can be found in a nest of loops that perform array calculations. Task parallelism can be found in many applications. It is particularly important in embedded systems because these systems often perform several different types of computation on data streams. In many ways, task-level parallelism is easy to exploit since they can be allocated to processors. However, task structure is not easily conveyed in C; programmers often resort to programming conventions combined with calls to interprocess communication routines to define tasks.

On the other hand, it comes with some liabilities. Of course, learning someone else’s code is always time-consuming. Furthermore, the code generally cannot be used as-is. Reference implementations are typically written to run on a large workstation with infinite memory; it is generally not designed to operate in real time. The code must often be restructured in many ways: eliminating features that will not be implemented; replacing heap allocation with custom memory management; improving cache utilization; function inlining; and many other tasks.

On the one hand, standards enable products and in particular systems-on-chips. Standards create large markets for particular types of functions: they allow devices to interoperate and they reassure customers that the device provides the required functions. Large markets help justify any system design project, but they are particularly important in system-on-chip (SoC) design. In order to cover the costs of SoC design and manufacturing, several million of the chips must be sold in many cases. Such large markets are generally created by standards.

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