By Manish Verma, Peter Marwedel
The layout of embedded platforms warrants a brand new standpoint due to the following purposes: first of all, sluggish and effort inefficient reminiscence hierarchies have already turn into the bottleneck of the embedded platforms. it's documented within the literature because the reminiscence wall challenge. Secondly, the software program operating at the modern embedded units is turning into more and more advanced. it's also good understood that no silver bullet exists to unravel the reminiscence wall challenge. consequently, this publication explores a collaborative procedure by means of featuring novel reminiscence hierarchies and software program optimization thoughts for the optimum usage of those reminiscence hierarchies. Linking reminiscence structure layout with memory-architecture acutely aware compilation leads to quick, energy-efficient and timing predictable reminiscence accesses. The review of the optimization concepts utilizing real-life benchmarks for a unmarried processor approach, a multiprocessor system-on-chip (SoC) and for a electronic sign processor procedure, studies major savings within the power intake and function development of those structures. The e-book offers quite a lot of optimizations, steadily expanding within the complexity of research and of reminiscence hierarchies. the ultimate bankruptcy covers optimization innovations for purposes together with a number of strategies present in most up-to-date embedded units. complex reminiscence Optimization innovations for Low strength Embedded Processors is designed for researchers, complier writers and embedded approach designers / architects who desire to optimize the strength and function features of the reminiscence subsystem.
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Additional resources for Advanced Memory Optimization Techniques for Low-Power Embedded Processors
The multi-process benchmark consists of an initiator process, a terminator process and a variable number of compute processes. 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.
The memory optimizer accesses the accurate energy model and performs transformations on the abstract syntax trees of the application. On termination, the memory optimizer 28 3 Memory Aware Compilation and Simulation Framework generates application source files one for each non-cacheable memory in the memory hierarchy. Since the multi-processor ARM simulator does not support complex memory hierarchies, it is sufficient to generate two source files, one for the shared main memory and one for the local scratchpad memory.
SA) 120 100 80 60 40 20 0 0 64 128 200 256 300 Scratchpad Size (Bytes) 400 512 0 1024 100 128 200 256 300 Scratchpad Size (bytes) 400 512 1024 (b) DSP (a) Edge Detection Fig. 5. Energy Comparison of Scratchpad Allocation Approaches detection and dsp benchmarks, respectively. The Frac. SA approach is allowed to allocate one memory object across the boundary of the scratchpad such that it is partially present in the scratchpad space and partially in the main memory space. Therefore, the approach always utilizes the entire scratchpad space to allocate memory objects.