ENTRA: Whole-systems energy transparency

Eder, Kerstin; Gallagher, John P.; López-García, Pedro; Muller, Henk; Banković, Zorana; Georgiou, Kyriakos; Haemmerlé, Rémy; Hermenegildo, Manuel V.; Kafle, Bishoksan; Kerrison, Steve; Kirkeby, Maja Hanne; Klemen, Maximiliano; Li, Xueliang; Liqat, Umer; Morse, Jeremy; Rhiger, Morten; Rosendahl, Mads

doi:10.1016/j.micpro.2016.07.003

Cited by 18 publications

(12 citation statements)

References 44 publications

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“…This model was further extended by Steinke et al [29] by including the switching activity on the data buses of the processor as the toggling bits take a major role in the energy consumption of the processor. In the EU ENTRA project [30], they investigated an energy transparent methodology for energy-aware development by using static analysis techniques for energy consumption estimations at compile time. In a first stage, they applied the static approach on the instruction (Instruction Set Architecture-ISA) level [31], and later extended it on the block level of the LLVM compiler [32].…”

Section: Worst-case Energy Consumption Analysismentioning

confidence: 99%

“…Overview of different methodologies from related research in comparison to our work. R1: Altenbernd et al[17], Bonenfant et al[18], Huybrechts et al[19] and Bachard et al[20]; R2: Tiwari et al[28]; R3: Steinke et al[29]; R4: EU ENTRA project[30]; R5: Jayaseelan et al[23]; R6: Wägemann et al[33]; R7: our work; 1 Hardware model is required; 2 Average-Case Energy Consumption; 3 LLVM's intermediate representation;4 Fallback strategy if the hardware model is not available; 5 ARTIST2 Language for WCET Flow Analysis.…”

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confidence: 99%

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Automated Testbench for Hybrid Machine Learning-Based Worst-Case Energy Consumption Analysis on Batteryless IoT Devices

et al. 2021

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Batteryless Internet-of-Things (IoT) devices need to schedule tasks on very limited energy budgets from intermittent energy harvesting. Creating an energy-aware scheduler allows the device to schedule tasks in an efficient manner to avoid power loss during execution. To achieve this, we need insight in the Worst-Case Energy Consumption (WCEC) of each schedulable task on the device. Different methodologies exist to determine or approximate the energy consumption. However, these approaches are computationally expensive and infeasible to perform on all type of devices; or are not accurate enough to acquire safe upper bounds. We propose a hybrid methodology that combines machine learning-based prediction on small code sections, called hybrid blocks, with static analysis to combine the predictions to a final upper bound estimation for the WCEC. In this paper, we present our work on an automated testbench for the Code Behaviour Framework (COBRA) that measures and profiles the upper bound energy consumption on the target device. Next, we use the upper bound measurements of the testbench to train eight different regression models that need to predict these upper bounds. The results show promising estimates for three regression models that could potentially be used for the methodology with additional tuning and training.

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Section: Worst-case Energy Consumption Analysismentioning

confidence: 99%

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confidence: 99%

Automated Testbench for Hybrid Machine Learning-Based Worst-Case Energy Consumption Analysis on Batteryless IoT Devices

et al. 2021

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“…The ENTRA (Whole-Systems ENergy TRAnsparency) project Deliverable D2.1 [Eder et al 2016] describes a similar mechanism to transfer information from source to machine code. Data and control flow properties are encoded as comments written as inline assembly expressions, relying on the compiler to preserve the local variables listed in the assembly expression.…”

Section: Related Workmentioning

confidence: 99%

Reconciling optimization with secure compilation

Cohen²,

Grandmaison³

et al. 2021

Proc. ACM Program. Lang.

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Software protections against side-channel and physical attacks are essential to the development of secure applications. Such protections are meaningful at machine code or micro-architectural level, but they typically do not carry observable semantics at source level. This renders them susceptible to miscompilation, and security engineers embed input/output side-effects to prevent optimizing compilers from altering them. Yet these side-effects are error-prone and compiler-dependent. The current practice involves analyzing the generated machine code to make sure security or privacy properties are still enforced. These side-effects may also be too expensive in fine-grained protections such as control-flow integrity. We introduce observations of the program state that are intrinsic to the correct execution of security protections, along with means to specify and preserve observations across the compilation flow. Such observations complement the input/output semantics-preservation contract of compilers. We introduce an opacification mechanism to preserve and enforce a partial ordering of observations. This approach is compatible with a production compiler and does not incur any modification to its optimization passes. We validate the effectiveness and performance of our approach on a range of benchmarks, expressing the secure compilation of these applications in terms of observations to be made at specific program points.

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“…We take up the additional challenge of embedding such mechanisms in a widely deployed compiler with minimal changes. Other work encode flow information using inline assembly [24] outside the IR [27,54], or using IR extensions and external transformations to update loop trip count information [46]. These approaches incur significant changes to optimization passes: they all come with a set of rules to transform control flow information along code transformations.…”

Section: Context and Related Workmentioning

confidence: 99%

Secure delivery of program properties through optimizing compilation

Heydemann

Grandmaison³

et al. 2020

Proceedings of the 29th International Conference on Compiler Construction

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Annotations and assertions capturing static program properties are ubiquitous, from robust software engineering to safety-critical or secure code. These may be functional or non-functional properties of control and data flow, memory usage, I/O and real time. We propose an approach to encode, translate, and preserve the semantics of both functional and non-functional properties along the optimizing compilation of C to machine code. The approach involves (1) capturing and translating source-level properties through lowering passes and intermediate representations, such that data and control flow optimizations will preserve their consistency with the transformed program, and (2) carrying properties and their translation as debug information down to machine code. Our experiments using LLVM validate the soundness, expressiveness and efficiency of the approach, considering a reference suite of functional properties as well as established security properties and applications hardened against side-channel attacks. CCS Concepts. • Software and its engineering → Compilers.

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ENTRA: Whole-systems energy transparency

Cited by 18 publications

References 44 publications

Automated Testbench for Hybrid Machine Learning-Based Worst-Case Energy Consumption Analysis on Batteryless IoT Devices

Automated Testbench for Hybrid Machine Learning-Based Worst-Case Energy Consumption Analysis on Batteryless IoT Devices

Reconciling optimization with secure compilation

Secure delivery of program properties through optimizing compilation

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