By Manish Verma, Peter Marwedel
The layout of embedded structures warrants a brand new viewpoint end result of the following purposes: first of all, sluggish and effort inefficient reminiscence hierarchies have already develop into the bottleneck of the embedded platforms. it really is documented within the literature because the reminiscence wall challenge. Secondly, the software program operating at the modern embedded units is changing into more and more complicated. it's also good understood that no silver bullet exists to unravel the reminiscence wall challenge. hence, this booklet explores a collaborative strategy by means of presenting novel reminiscence hierarchies and software program optimization innovations for the optimum usage of those reminiscence hierarchies. Linking reminiscence structure layout with memory-architecture conscious compilation ends up in quick, energy-efficient and timing predictable reminiscence accesses. The evaluate of the optimization suggestions utilizing real-life benchmarks for a unmarried processor process, a multiprocessor system-on-chip (SoC) and for a electronic sign processor method, studies major savings within the strength intake and function development of those structures. The ebook offers quite a lot of optimizations, gradually expanding within the complexity of research and of reminiscence hierarchies. the ultimate bankruptcy covers optimization concepts for purposes which include a number of methods present in most recent embedded units. complex reminiscence Optimization concepts for Low energy Embedded Processors is designed for researchers, complier writers and embedded approach designers / architects who desire to optimize the power and function features of the reminiscence subsystem.
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Additional info for Advanced Memory Optimization Techniques for Low-Power Embedded Processors
Lastly, it is observed that significant energy savings of more than 70% could be achieved by the introduction of a scratchpad in the memory hierarchy of the system. Comparison of the Scratchpad Allocation Approaches: Next, we present a comparison of the ILP based optimal non-overlayed scratchpad allocation (SA) approach and the greedy algorithm based fractional scratchpad allocation (Frac. SA) approach. 5(b) present the comparison of the two approaches for edge Energy (SA) 11 10 Energy (Frac. 6 Experimental Results 9 8 7 6 5 4 3 2 1 0 43 Energy (Frac.
The loop caches, is required. This kind of flexibility is missing in previously published memory simulation frameworks which tend to focus on one particular component of the memory hierarchy. The two important advantages of MEMSIM over other known memory simulators, such as Dynero , are its cycle true simulation capability and configurability. Currently, MEMSIM supports a number of different memories with different access characteristics, such as caches, loop caches, scratchpads, DRAMs and Flash memories.
Even though the approach is sufficiently fine grained, it is unable to find a different allocation when increasing the scratchpad size from 100 bytes to 128 bytes. Second, it is observed that for each scratchpad size the normalized energy values are lower than the corresponding execution time values. This implies that the utilization of the scratchpad has more impact on reducing the total energy consumption of the system than on reducing the execution time of the application. This observation is justified because the difference in energy per access to the main memory and the scratchpad is larger than the difference in their access times.
Advanced Memory Optimization Techniques for Low-Power Embedded Processors by Manish Verma, Peter Marwedel