Algorithms for energy optimization using processor instructions

Seth, Anil; Keskar, Ravindra B.; Venugopal, R.

doi:10.1145/502217.502252

Cited by 9 publications

(3 citation statements)

References 5 publications

(3 reference statements)

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“…Seth et al [22] explored the possibility of using the compiler to insert idle instructions automatically, increasing the execution time up to a set limit. Using the SIMD pipeline has been shown to decrease energy consumption [23] by roughly 25%.…”

Section: Optimisations Targeting Energymentioning

confidence: 99%

Identifying Compiler Options to Minimize Energy Consumption for Embedded Platforms

Pallister

Hollis

Bennett

2013

The Computer Journal

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This paper presents an analysis of the energy consumption of an extensive number of the optimisations a modern compiler can perform. Using GCC as a test case, we evaluate a set of ten carefully selected benchmarks for five different embedded platforms. A fractional factorial design is used to systematically explore the large optimisation space (2 82 possible combinations), whilst still accurately determining the effects of optimisations and optimisation combinations. Hardware power measurements on each platform are taken to ensure all architectural effects on the energy consumption are captured. We show that fractional factorial design can find more optimal combinations than relying on built in compiler settings. We explore the relationship between runtime and energy consumption, and identify scenarios where they are and are not correlated. A further conclusion of this study is the structure of the benchmark has a larger effect than the hardware architecture on whether the optimisation will be effective, and that no single optimisation is universally beneficial for execution time or energy consumption.

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Section: Optimisations Targeting Energymentioning

confidence: 99%

Identifying Compiler Options to Minimize Energy Consumption for Embedded Platforms

Pallister

Hollis

Bennett

2013

The Computer Journal

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“…Other software optimizations targeting energy have considered automatically inserting idle instructions [20], instruction scheduling [18], use of SIMD [11] and exploiting differences in functional units [6].…”

Section: Related Workmentioning

confidence: 99%

A high-level model of embedded flash energy consumption

Pallister

Eder

Hollis

et al. 2014

Proceedings of the 2014 International Conference on Compilers, Architecture and Synthesis for Embedded Systems

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The alignment of code in the flash memory of deeply embedded SoCs can have a large impact on the total energy consumption of a computation. We investigate the effect of code alignment in six SoCs and find that a large proportion of this energy (up to 15% of total SoC energy consumption) can be saved by changes to the alignment. A flexible model is created to predict the read-access energy consumption of flash memory on deeply embedded SoCs, where code is executed in place. This model uses the instruction level memory accesses performed by the processor to calculate the flash energy consumption of a sequence of instructions. We derive the model parameters for five SoCs and validate them. The error is as low as 5%, with a 11% average normalized RMS deviation overall. The scope for using this model to optimize code alignment is explored across a range of benchmarks and SoCs. Analysis shows that over 30% of loops can be better aligned. This can significantly reduce energy while increasing code size by less than 4%. We conclude that this effect has potential as an effective optimization, saving significant energy in deeply embedded SoCs

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“…Hardware consumes a significant amount of energy due to the software it runs, and thus the same hardware configuration running different software implementations of the same functionality will have different energy consumption figures (Lafond, 2009). There is a considerable amount of work on optimizing the energy consumption of software by studying processor instructions (Seth, Keskar, & Venugopal, 2001), a family of processors (Russel & Jacome, 1998), or the memory management (Fan, Ellis, & Lebeck, 2001). It is also very useful to evaluate such figures for identifying the best high level software configuration.…”

Section: Related Workmentioning

confidence: 99%

Towards Sustainable Development of Energy-Aware Systems

Sere

Petre

2010

International Journal of Embedded and Real-Time Communication Systems

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Smart devices have pervaded our society and encouraged lifestyles that depend on them. One of the fundamental requirements for a successful dependency is that the general public be aware of the energy limitations of these devices and to stay in control of energy consumption. In this paper, the authors propose a formal specification method that takes energy into account. They propose two development approaches that can use these specifications to develop energy-aware systems in a sustainable manner.

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Algorithms for energy optimization using processor instructions

Cited by 9 publications

References 5 publications

Identifying Compiler Options to Minimize Energy Consumption for Embedded Platforms

Identifying Compiler Options to Minimize Energy Consumption for Embedded Platforms

A high-level model of embedded flash energy consumption

Towards Sustainable Development of Energy-Aware Systems

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