Towards automatic significance analysis for approximate computing

Vassiliadis, Vassilis; Riehme, Jan; Deussen, Jens; Parasyris, Konstantinos; Antonopoulos, Christos D.; Bellas, Nikolaos; Lalis, Spyros; Naumann, Uwe

doi:10.1145/2854038.2854058

Cited by 36 publications

(19 citation statements)

References 31 publications

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“…The programmer must analyze the workloads to identify the criticality of the data structures and therefore select in which reliability domain to allocate the data. Recent research work [14] presented an automated technique to identify the resilience of data structures through analytical models, however, analysis of the data criticality is out of the scope of this paper.…”

Section: Methodsmentioning

confidence: 99%

Shimmer: Implementing a Heterogeneous-Reliability DRAM Framework on a Commodity Server

Tovletoglou

Mukhanov

Nikolopoulos

et al. 2019

IEEE Comput. Arch. Lett.

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In this paper, we present the implementation of a heterogeneous-reliability DRAM framework, Shimmer, on a commodity server with a fully fledged OS. Shimmer enables splitting of DRAM into multiple domains with varying reliability and allocation of data depending on their criticality. Compared to existing studies which use simulators, we consider practical restrictions stemming from the real hardware and investigate methods to overcome them. In particular, we reveal that the implementation of the heterogeneous-reliability memory framework requires disabling of the hardware memory interleaving, which results in a significant degradation of the system performance. To overcome the induced performance loss, we develop a software-based interleaving. We evaluate the performance, power and energy of the server using 35 benchmarks across three memory configurations: the baseline configuration; with disabled hardware memory interleaving and Shimmer with software-based memory interleaving. Our results show that Shimmer introduces a minor 6% performance overhead, while reducing the average DRAM power by 19.9% when memory operates under relaxed refresh rate and lowered memory supply voltage. As one of our main contributions we demonstrate that a heterogeneous-reliability framework based on Shimmer can be realized on a commodity server and save 9.1% of the total processor and memory energy.

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Section: Methodsmentioning

confidence: 99%

Shimmer: Implementing a Heterogeneous-Reliability DRAM Framework on a Commodity Server

Tovletoglou

Mukhanov

Nikolopoulos

et al. 2019

IEEE Comput. Arch. Lett.

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“…If the obtained distance is lower or equal to d, the configuration w j sim is kept as a neighbouring configuration for kriging. This value is stored in W tmp as well as the corresponding metric value in λ tmp (lines [11][12]. If enough surrounding configurations have already been simulated, that is to say if N n is higher than the minimum number of neighbouring points N n,min (line 17), kriging is applied, else the configuration is simulated.…”

Section: ) Proposed Algorithmmentioning

confidence: 99%

“…Analytical methods mathematically express the quality metric depending on the approximation sources. Approaches based on interval arithmetic have been used to determine the output error bounds [10] or the computation significance [11]. Generalizing theses approaches to other quality metric or to other approximation sources is still a challenge.…”

Section: Introductionmentioning

confidence: 99%

Fast Kriging-based Error Evaluation for Approximate Computing Systems

Bonnot

Ménard

Desnos

2020

2020 Design, Automation &Amp; Test in Europe Conference &Amp; Exhibition (DATE)

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Approximate computing techniques trade-off the performance of an application for its accuracy. The challenge when implementing approximate computing in an application is to efficiently evaluate the quality at the output of the application to optimize the noise budgeting of the different approximation sources. It is commonly achieved with an optimization algorithm to minimize the implementation cost of the application subject to a quality constraint. During the optimization process, numerous approximation configurations are tested, and the quality at the output of the application is measured for each configuration with simulations. The optimization process is a time-consuming task. We propose a new method for infering the accuracy or quality metric at the output of an application using kriging, a geostatistical method.

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“…Significance analysis (or "sensitivity analysis") is a technique used to determine how different values of independent variables impact a particular resultant variable (Vassiliadis et al 2016). It predicts the outcome of changes made on the variables by revealing how accurate each of the used variables needs to be and aids in the understanding of how the program behaves based on those variables.…”

Section: Significance Analysismentioning

confidence: 99%

Ssa-Ac

Metwalli

Hara-Azumi

2019

ACM Trans. Des. Autom. Electron. Syst.

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Recently, the quest to reduce energy consumption in digital systems has been the subject of a number of ongoing studies. One of the most researched focuses is approximate computing (AC). AC is a new computing paradigm in both hardware and software designs that aim to achieve energy-efficient digital systems. Although a variety of AC techniques have been studied so far, the main question, "How (in which section) can a program or a circuit be approximated?," has not been answered yet. This work addresses the above issue by developing a software framework Static Significance Analysis for Approximate Computing (SSA-AC) to analyze the target application program and guide the designers to identify parts of the program to which approximation can or cannot be applied. SSA-AC statically analyzes the significance of variables in the precise version of the program and thus needs no trial-and-error evaluation or specific test data. Experimental results show that SSA-AC can successfully extract the significance ranking of inputs/variables to be approximated in much shorter time than existing statistical works that are inevitably data dependent.

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Towards automatic significance analysis for approximate computing

Cited by 36 publications

References 31 publications

Shimmer: Implementing a Heterogeneous-Reliability DRAM Framework on a Commodity Server

Shimmer: Implementing a Heterogeneous-Reliability DRAM Framework on a Commodity Server

Fast Kriging-based Error Evaluation for Approximate Computing Systems

Ssa-Ac

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