Energy Aware Computing through Probabilistic Switching: A Study of Limits

Palem, Krishna V.

doi:10.1109/tc.2005.145

Cited by 158 publications

(92 citation statements)

References 22 publications

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“…Though, most existing work [84]- [86] introduces errors by intelligently overscaling voltage supplies, there has been some work in the direction of introducing error into a system via manipulation of its logic-function, for adders [87], [88] as well as generic combinational logic [17]. Let us consider an example functionally underdesigned integer multiplier circuit [15] with potentially erroneous operation.…”

Section: Implications For Circuits and Architecturesmentioning

confidence: 99%

Underdesigned and Opportunistic Computing in Presence of Hardware Variability

Gupta

Agarwal

Dolecek

et al. 2013

IEEE Trans. Comput.-Aided Des. Integr. Circuits Syst.

138

116

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Abstract-Microelectronic circuits exhibit increasing variations in performance, power consumption, and reliability parameters across the manufactured parts and across use of these parts over time in the field. These variations have led to increasing use of overdesign and guardbands in design and test to ensure yield and reliability with respect to a rigid set of datasheet specifications. This paper explores the possibility of constructing computing machines that purposely expose hardware variations to various layers of the system stack including software. This leads to the vision of underdesigned hardware that utilizes a software stack that opportunistically adapts to a sensed or modeled hardware. The envisioned underdesigned and opportunistic computing (UnO) machines face a number of challenges related to the sensing infrastructure and software interfaces that can effectively utilize the sensory data. In this paper, we outline specific sensing mechanisms that we have developed and their potential use in building UnO machines.

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Section: Implications For Circuits and Architecturesmentioning

confidence: 99%

Underdesigned and Opportunistic Computing in Presence of Hardware Variability

Gupta

Agarwal

Dolecek

et al. 2013

IEEE Trans. Comput.-Aided Des. Integr. Circuits Syst.

138

116

View full text Add to dashboard Cite

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“…Other approaches consider scaling the voltage below the minimum voltage supply value required, so as to trade accuracy for power [26]. However, the observation that errors in bits of higher order affect the quality of the solution more than the lower order bits do led to operating adders of more significant bits with a higher voltage and downscale the voltages for lower bits [27].…”

Section: Approximate Computing Methods For Dsp Systemsmentioning

confidence: 99%

A novel method for the approximation of multiplierless constant matrix vector multiplication

Aksoy¹,

Flores

Monteiro

2016

J Embedded Systems

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Since human beings have limited perceptual abilities, in many digital signal processing (DSP) applications, e.g., image and video processing, the outputs do not need to be computed accurately. Instead, their computation can be approximated so that the area, delay, and/or power dissipation of the design can be reduced. This paper presents an approximation algorithm, called AURA, for the multiplierless design of the constant matrix vector multiplication (CMVM) which is a ubiquitous operation in DSP systems. AURA aims to tune the constants such that the resulting matrix leads to a CMVM design which requires the fewest adders/subtractors, satisfying the given error constraints. This paper also introduces its modified version, called AURA-DC, which can reduce the delay of the CMVM operation with a small increase in the number of adders/subtractors. Experimental results show that the proposed algorithms yield significant reductions in the number of adders/subtractors with respect to the original realizations without violating the error constraints, and consequently lead to CMVM designs with less area, delay, and power dissipation. Moreover, they can generate alternative CMVM designs under different error constraints, enabling a designer to choose the one that fits best in an application.

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“…Researchers have previously proposed multiple hardware architecture designs that improve the performance or energy consumption of processors by reducing precision or increasing the incidence of error [8,9,15,18,19,28,30,35,44,46,47]. Researchers have also proposed a variety of approximate DRAM and SRAM designs.…”

Section: Related Workmentioning

confidence: 99%

“…Motivated by the benefits available via relaxing stringent correctness and precision requirements, researchers have proposed a variety of approximate computing platforms [6,9,10,19,22,26,28,30,36,45,47]. Many of these platforms are populated with components that feature higher error rates and/or less accurate execution in return for reduced energy consumption and/or increased performance.…”

Section: Introductionmentioning

confidence: 99%

Approximate computation with outlier detection in Topaz

Achour

Rinard

2015

SIGPLAN Not.

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We present Topaz, a new task-based language for computations that execute on approximate computing platforms that may occasionally produce arbitrarily inaccurate results. Topaz maps tasks onto the approximate hardware and integrates the generated results into the main computation. To prevent unacceptably inaccurate task results from corrupting the main computation, Topaz deploys a novel outlier detection mechanism that recognizes and precisely reexecutes outlier tasks. Outlier detection enables Topaz to work effectively with approximate hardware platforms that have complex fault characteristics, including platforms with bit pattern dependent faults (in which the presence of faults may depend on values stored in adjacent memory cells). Our experimental results show that, for our set of benchmark applications, outlier detection enables Topaz to deliver acceptably accurate results (less than 1% error) on our target approximate hardware platforms. Depending on the application and the hardware platform, the overall energy savings range from 5 to 13 percent. Without outlier detection, only one of the applications produces acceptably accurate results.

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Energy Aware Computing through Probabilistic Switching: A Study of Limits

Cited by 158 publications

References 22 publications

Underdesigned and Opportunistic Computing in Presence of Hardware Variability

Underdesigned and Opportunistic Computing in Presence of Hardware Variability

A novel method for the approximation of multiplierless constant matrix vector multiplication

Approximate computation with outlier detection in Topaz

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