Impact of Software Approximations on the Resiliency of a Video Summarization System

Venkatagiri, Radha; Swaminathan, Karthik; Lin, Chung-Ching; Wang, Liang; Buyuktosunoglu, Alper; Bose, Pradip; Adve, Sarita V.

doi:10.1109/dsn.2018.00067

Cited by 9 publications

(3 citation statements)

References 53 publications

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“…We represent soft errors as single-bit flips in hardware registers, which is consistent with most other papers studying the effects of soft errors [7], [16], [25], [111]. While we focus on single-bit flips, Equation (2) can be extended to accommodate a multi-bit fault model.…”

Section: Fault Injection (Fi)mentioning

confidence: 98%

Characterizing and Improving the Resilience of Accelerators in Autonomous Robots

Shah¹,

Xue²,

Pattabiraman³

et al. 2021

Preprint

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Motion planning is a computationally intensive and well-studied problem in autonomous robots. However, motion planning hardware accelerators (MPA) must be soft-error resilient for deployment in safety-critical applications, and blanket application of traditional mitigation techniques is ill-suited due to cost, power, and performance overheads. We propose Collision Exposure Factor (CEF), a novel metric to assess the failure vulnerability of circuits processing spatial relationships, including motion planning. CEF is based on the insight that the safety violation probability increases with the surface area of the physical space exposed by a bit-flip. We evaluate CEF on four MPAs. We demonstrate empirically that CEF is correlated with safety violation probability, and that CEF-aware selective error mitigation provides 12.3×, 9.6×, and 4.2× lower Failures-In-Time (FIT) rate on average for the same amount of protected memory compared to uniform, bit-position, and access-frequency-aware selection of critical data. Furthermore, we show how to employ CEF to enable fault characterization using 23, 000× fewer fault injection (FI) experiments than exhaustive FI, and evaluate our FI approach on different robots and MPAs. We demonstrate that CEF-aware FI can provide insights on vulnerable bits in an MPA while taking the same amount of time as uniform statistical FI. Finally, we use the CEF to formulate guidelines for designing soft-error resilient MPAs.

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Section: Fault Injection (Fi)mentioning

confidence: 98%

Characterizing and Improving the Resilience of Accelerators in Autonomous Robots

Shah¹,

Xue²,

Pattabiraman³

et al. 2021

Preprint

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show abstract

“…While typically slower than the previous group, these techniques employ error injections at different hardware and software abstractions [18,24,31,44,54,64,65,91,109,122]. Some rely predominantly on statistical error injections for vulnerability analysis [25,41,60,62,118]. Others combine program analysis with selected error-injection campaigns [2,49,54,61,108,117].…”

Section: Related Workmentioning

confidence: 99%

Minotaur

Mahmoud

Venkatagiri

Ahmed

et al. 2019

Proceedings of the Twenty-Fourth International Conference on Architectural Support for Programming Languages and Operating Syst

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With the end of conventional CMOS scaling, efficient resiliency solutions are needed to address the increased likelihood of hardware errors. Silent data corruptions (SDCs) are especially harmful because they can create unacceptable output without the user's knowledge. Several resiliency analysis techniques have been proposed to identify SDC-causing instructions, but they remain too slow for practical use and/or sacrifice accuracy to improve analysis speed. We develop Minotaur, a novel toolkit to improve the speed and accuracy of resiliency analysis. The key insight behind Minotaur is that modern resiliency analysis has many conceptual similarities to software testing; therefore, adapting techniques from the rich software testing literature can lead to principled and significant improvements in resiliency analysis. Minotaur identifies and adapts four concepts from software testing: 1) it introduces the concept of input quality criteria for resiliency analysis and identifies PC coverage as a simple but effective criterion; 2) it creates (fast) minimized inputs from (slow) standard benchmark inputs, using the input quality criteria to assess the goodness of the created input; 3) it adapts the concept of test case prioritization to prioritize error injections and invoke early termination for a given instruction to speed up error-injection campaigns; and 4) it further adapts test case or input prioritization to accelerate SDC discovery across multiple inputs. We evaluate Minotaur by applying it to Approxilyzer, a state-of-the-art resiliency analysis tool. Minotaur's first three techniques speed up Approxilyzer's resiliency analysis by

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“…The mandatory part must be executed entirely to produce an acceptable result, while optional parts will be executed for refining the result. Application scenarios involve target localization task that is carried out by an unmanned aerial vehicle [6], [7]. Wei et al introduce the concept of AC to handle uncertainties in energy availability of a real-time system.…”

Section: Introductionmentioning

confidence: 99%

SENAS: Security driven ENergy Aware Scheduler for Real Time Approximate Computing Tasks on Multi-Processor Systems

Guha

Saha

McDonald-Maier

2022

2022 IEEE 28th International Symposium on on-Line Testing and Robust System Design (IOLTS)

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Present day real time approximate computing applications like image and video processing involves execution of a set of tasks before a certain amount of time or deadline. In addition to this, present day systems are associated with strict energy budget that cannot be changed post deployment. The tasks comprises of a mandatory and optional part. Completion of all mandatory portions of all tasks before deadline is much more important than result accuracy in such real time approximate computing applications. Based on the energy budget, the optional portions can be executed that determines the quality of service (QoS) of the system. In ideal scenario, sufficient energy budget is present that ensures completion of both mandatory and optional portions in a system with a pre-determined number of processors. However, if fault or malware attack occurs on one or more processors, then the system will cease to work and results may be fatal. In this work, we consider such a scenario where the processors may be faulty and stop functioning in post deployment phases or some malware may cause unexpected delays in processing or may cause unexpected power draining at runtime that will prevent the system from meeting its deadline. We propose a Security driven ENergy Aware Scheduler (SENAS) that works as a self aware agent. Initially, based on the available energy budget, SENAS determines which task is to be executed in which processor of a system. At runtime, SENAS constantly monitors the working of the processors and on detecting any anomaly in any of the processors, it reschedules its tasks at runtime by reducing execution of the optional portions of the tasks and ensuring completion before deadline with high QoS.

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Impact of Software Approximations on the Resiliency of a Video Summarization System

Cited by 9 publications

References 53 publications

Characterizing and Improving the Resilience of Accelerators in Autonomous Robots

Characterizing and Improving the Resilience of Accelerators in Autonomous Robots

Minotaur

SENAS: Security driven ENergy Aware Scheduler for Real Time Approximate Computing Tasks on Multi-Processor Systems

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