A Taxonomy of General Purpose Approximate Computing Techniques

Moreau, Thierry; Miguel, Joshua San; Wyse, Mark; Bornholt, James; Alaghi, Armin; Ceze, Luís; Jerger, Natalie Enright; Sampson, Adrian

doi:10.1109/les.2017.2758679

Cited by 30 publications

(25 citation statements)

References 35 publications

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“…To understand the language of the device we need the equivalent of Shannon's analysis of the differential analyzer [73]. The encoding-decoding pair is also linked to the "natural basis of computation" [74] of a device, which refers to the description of the device behavior suited for the computation purposes.…”

Section: Analog Computationmentioning

confidence: 99%

Memristors for the Curious Outsiders

Caravelli

Carbajal

2018

Technologies

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We present both an overview and a perspective of recent experimental advances and proposed new approaches to performing computation using memristors. A memristor is a 2-terminal passive component with a dynamic resistance depending on an internal parameter. We provide an brief historical introduction, as well as an overview over the physical mechanism that lead to memristive behavior. This review is meant to guide nonpractitioners in the field of memristive circuits and their connection to machine learning and neural computation.

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Section: Analog Computationmentioning

confidence: 99%

Memristors for the Curious Outsiders

Caravelli

Carbajal

2018

Technologies

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“…2) Deterministic and Non-deterministic: The classification of deterministic and non-deterministic for approximate computing depends on the output of the approximated design [42]. A deterministic design repeatedly returns the same output when given the same input as shown in Fig.…”

Section: A Design Objectivesmentioning

confidence: 99%

Security in Approximate Computing and Approximate Computing for Security: Challenges and Opportunities

Liu

O’Neill

et al. 2020

Proc. IEEE

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Approximate computing, an advanced computational technique which returns inaccurate but acceptable results instead of exact results, has emerged as a new preferable paradigm over traditional computing architectures for energy efficient system designs. It is crucial for nanoscale integrated circuits (ICs) to achieve high speed and low power, where some intrinsic errors are acceptable, such as (deep-) machine learning, image processing, communication and other error-tolerant and cognitive applications. However, approximate computing also introduces security vulnerabilities mainly due to the uncertainty and unpredictability of intrinsic errors during approximate execution which may be indistinguishable using malicious modification of the accurate result. On the other hand, interestingly, approximate computing can also provide new approaches for security. Existing literature in approximate computing covers threat models, countermeasures, and evaluations, but lacks a framework for analysis and comparison. In this paper, we provide a classification of the state of the art in this research field, including threat models in approximate computing and promising security approaches using approximate computing. Index Terms-Approximate computing, hardware security, cryptography I. INTRODUCTION N the last decade, various advanced computing systems, including supercomputers, ubiquitous computing centers, and servers, have been developed and widely deployed. Unfortunately, Moore's law is approaching its limitation [1], and conventional computing techniques are not able to provide higher computing performance under the restriction of power consumption. Therefore, new nanoscale computing paradigms are urgently required for low power and high performance computing systems. Hence, appropriate reduction of the computational accuracy could effectively improve the performance of computing systems without sacrificing functionality and perception.

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“…Although this work focuses on CPUs, recent resiliency analyses on GPUs [55,62,83], for example, can potentially benefit from the concepts of Minotaur to improve runtime and/or accuracy. Approximate computing: Many techniques have been proposed that leverage approximate computing at the level of software [8,11,23,71,85,100,106,110,114,121,125,127], programming languages [15,20,74,86,87,101,102] and hardware [5,11,16,33,42,46,53,56,77,103,105,112,126,128,133] for improved performance, energy, or reliability. Criticality-testing [3,4,21,75,84,97,115] of approximate computations is important for many domains.…”

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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A Taxonomy of General Purpose Approximate Computing Techniques

Cited by 30 publications

References 35 publications

Memristors for the Curious Outsiders

Memristors for the Curious Outsiders

Security in Approximate Computing and Approximate Computing for Security: Challenges and Opportunities

Minotaur

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