arXiv subjects

Kerstin Eder

Publications and source records attributed to Kerstin Eder.

75 records · Page 5Linked to original sources

arXiv2014

Optimizing the flash-RAM energy trade-off in deeply embedded systems

Deeply embedded systems often have the tightest constraints on energy consumption, requiring that they consume tiny amounts of current and run on batteries for years. However, they typically execute code directly from flash, instead of the more energy efficient RAM. We implement a novel compiler optimization that exploits the relative efficiency of RAM by statically moving carefully selected basic blocks from flash to RAM. Our technique uses integer linear programming, with an energy cost model to select a good set of basic blocks to place into RAM, without impacting stack or data storage. We evaluate our optimization on a common ARM microcontroller and succeed in reducing the average power consumption by up to 41% and reducing energy consumption by up to 22%, while increasing execution time. A case study is presented, where an application executes code then sleeps for a period of time. For this example we show that our optimization could allow the application to run on battery for up to 32% longer. We also show that for this scenario the total application energy can be reduced, even if the optimization increases the execution time of the code.

cs.OH↗

arXiv2014

EACOF: A Framework for Providing Energy Transparency to enable Energy-Aware Software Development

Making energy consumption data accessible to software developers is an essential step towards energy efficient software engineering. The presence of various different, bespoke and incompatible, methods of instrumentation to obtain energy readings is currently limiting the widespread use of energy data in software development. This paper presents EACOF, a modular Energy-Aware Computing Framework that provides a layer of abstraction between sources of energy data and the applications that exploit them. EACOF replaces platform specific instrumentation through two APIs - one accepts input to the framework while the other provides access to application software. This allows developers to profile their code for energy consumption in an easy and portable manner using simple API calls. We outline the design of our framework and provide details of the API functionality. In a use case, where we investigate the impact of data bit width on the energy consumption of various sorting algorithms, we demonstrate that the data obtained using EACOF provides interesting, sometimes counter-intuitive, insights. All the code is available online under an open source license. http://github.com/eacof

cs.SE↗

arXiv2014

A high-level model of embedded flash energy consumption

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.

cs.AR↗