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

By Manish Verma, Peter Marwedel

The layout of embedded structures warrants a brand new viewpoint end result of the following purposes: to begin with, gradual and effort inefficient reminiscence hierarchies have already develop into the bottleneck of the embedded structures. it truly 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 advanced. it's also good understood that no silver bullet exists to unravel the reminiscence wall challenge. accordingly, this publication explores a collaborative process by means of presenting novel reminiscence hierarchies and software program optimization thoughts for the optimum usage of those reminiscence hierarchies. Linking reminiscence structure layout with memory-architecture conscious compilation ends up in speedy, energy-efficient and timing predictable reminiscence accesses. The assessment of the optimization recommendations utilizing real-life benchmarks for a unmarried processor approach, a multiprocessor system-on-chip (SoC) and for a electronic sign processor method, reviews major savings within the strength intake and function development of those platforms. The publication offers a variety of optimizations, gradually expanding within the complexity of research and of reminiscence hierarchies. the ultimate bankruptcy covers optimization thoughts for purposes including a number of approaches present in newest embedded units. complex reminiscence Optimization suggestions for Low strength Embedded Processors is designed for researchers, complier writers and embedded procedure designers / architects who desire to optimize the strength and function features of the reminiscence subsystem.

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Extra info for Advanced Memory Optimization Techniques for Low-Power Embedded Processors

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Second, a greedy approach [30], which fills the knapsack with objects in the descending order of their valence (profit per unit size pk /wk ) and breaking only the last object if it does not fit completely, finds the optimal solution for the fractional knapsack problem. This implies that the greedy approach for FKP can be also use to solve the Frac. SA problem, as it allows the factional allocation of a maximum of one memory object. Third, the total profit obtained by solving the fractional knapsack problem is larger than or equal to the profit of the corresponding knapsack problem as the former is a relaxation of the latter.

1 depicts the scenario when an array A is partially allocated to the scratchpad memory. It should be noted that this seamless scratchpad and main memory accesses may not be available in all systems. The Frac. SA problem demonstrates a few interesting properties. First, it is similar to the fractional knapsack problem (FKP) [30], a variant of the KP, which allows the knapsack to be filled with partial objects. Second, a greedy approach [30], which fills the knapsack with objects in the descending order of their valence (profit per unit size pk /wk ) and breaking only the last object if it does not fit completely, finds the optimal solution for the fractional knapsack problem.

Though the energy model is not as detailed as the previous measurement based instruction level energy model, it is sufficiently accurate for a simple ARM7 processor. 13 µm technology. In addition, the framework includes energy models for the ST-Bus also obtained from STMicroelectronics. However, no energy model is included for the AMBA-Bus. A detailed discussion on the energy models for the multi-processor simulation framework can be found in [78]. Fig. 6. 2 Compilation Framework The compilation framework for the multi-processor ARM based systems includes a source level memory optimizer which is based on the ICD-C compilation framework [54] and GCC’s cross compiler tool chain for ARM processors.

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