By Manish Verma, Peter Marwedel
This ebook proposes novel reminiscence hierarchies and software program optimization thoughts for the optimum usage of reminiscence hierarchies. It offers a variety of optimizations, steadily expanding within the complexity of study and of reminiscence hierarchies. the ultimate bankruptcy covers optimization thoughts for purposes together with a number of approaches present in most up-to-date embedded devices.
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However, due to copyright reasons, we are forbidden to report exact energy values. Therefore, only normalized energy values for the data memory subsystem of the M5 DSP will be reported in this work. The compilation framework for the M5 DSP is similar to that for the uni-processor ARM based system. The only significant difference between the two is that the compiler for the M5 DSP uses a phase coupled code generator . The code generation is divided into four subtasks: code selection (CS), instruction scheduling (IS), register allocation (RA) 30 3 Memory Aware Compilation and Simulation Framework and address code generation (ACG).
The benchmark is simulated on the homogenous multi-processor ARM based system such that each process is mapped to a unique ARM processor. The processors in the multi-procesor system are named according to the mapped process. Each processor has its own local scratchpad memory, while all of them access a shared main memory. 2. 7 presents the total energy consumption values for the benchmark when the number of the compute processors and the size of the local scratchpad memory is varied. 0 0 128 256 512 1024 2048 Scratchpad Size (bytes) 4096 8192 16384 Fig.
7. Multi-Process Edge Detection Application Multi-Process Edge Detection Benchmark: The memory optimizations for the multi-processor ARM based system are evaluated for the multi-process edge detection benchmark. The original benchmark was obtained from  and was parallelized so that it can execute on a multi-processor system. The multi-processor benchmark consists of an initiator process, a terminator process and a variable number of compute processes to detect the edges in the input tomographic images.