Advanced Memory Optimization Techniques for Low-Power by Manish Verma, Peter Marwedel

By Manish Verma, Peter Marwedel

The layout of embedded platforms warrants a brand new standpoint end result of the following purposes: first of all, gradual 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 working at the modern embedded units is turning into more and more advanced. it's also good understood that no silver bullet exists to resolve the reminiscence wall challenge. for this reason, this booklet explores a collaborative procedure by means of providing novel reminiscence hierarchies and software program optimization innovations for the optimum usage of those reminiscence hierarchies. Linking reminiscence structure layout with memory-architecture acutely aware compilation ends up in quick, energy-efficient and timing predictable reminiscence accesses. The review of the optimization ideas utilizing real-life benchmarks for a unmarried processor process, a multiprocessor system-on-chip (SoC) and for a electronic sign processor procedure, reviews major discount rates within the strength intake and function development of those structures. The publication provides a variety of optimizations, gradually expanding within the complexity of research and of reminiscence hierarchies. the ultimate bankruptcy covers optimization concepts for purposes inclusive of a number of approaches present in newest embedded units. complex reminiscence Optimization innovations for Low strength Embedded Processors is designed for researchers, complier writers and embedded method designers / architects who desire to optimize the power and function features of the reminiscence subsystem.

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A graphical user interface is provided so that the user can comfortably select the components that should be simulated in the memory hierarchy. The GUI generates a description of the memory hierarchy in the form of an XML file. Please refer to [133] for a complete description of the memory hierarchy simulator. Benchmark Suite: The presentation of the compilation and simulation framework is not complete without the description of the benchmarks that can be compiled and simulated. Our research compiler ENCC has matured into a stable compiler supporting all ANSI-C data types and can compile 26 3 Memory Aware Compilation and Simulation Framework and optimize applications from the Mediabench [87], MiBench [51] and UTDSP [73] benchmark suites.

A maximum difference of 2% is observed for the dsp benchmark at 512 bytes scratchpad sizes. Therefore, if the system architecture permits allocation of a memory object across the boundary of a scratchpad, then the Frac. SA approach having a polynomial time complexity should be used to replace the SA approach. Energy (Frac. 0 adpcm dsp Energy (SA) edge detection Exec. Time (Frac. SA) histogram media Exec. Time (SA) mpeg multisort average Fig. 6. Overall Comparison of the Scratchpad Allocation Approaches Next, a comparison of the two scratchpad allocation approaches over all benchmarks is presented.

The terminator process then reads the image from the output buffer and then writes it to a backing store. The synchronization between the initiator process and the compute processes is handled by a pair of semaphores. Similarly, another of pair semaphores is used to maintain the synchronization between the compute processes and the terminator process. 3 M5 DSP 29 Slice Program Memory Data Memory AGU Register File InterConnectivity Program Control Data Paths Vector Engine Scalar Engine Fig. 8. Block Diagram of M5 DSP Fig.

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