Data clustering for efficient approximate computing

Jordan, Michael Guilherme; Brandalero, Marcelo; Malfatti, Guilherme Meneguzzi; Oliveira, Geraldo F.; Lorenzon, Arthur F.; Silva, Bruno C. da; Carro, Luigi; Rutzig, Mateus Beck; Beck, Antonio Carlos Schneider

doi:10.1007/s10617-019-09228-z

Cited by 4 publications

(5 citation statements)

References 32 publications

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“…The corresponding result is returned if an exact match is found; otherwise, the actual function call is made, which returns the result and updates the LUT 21 . Figure 5B shows the second scenario, which is commonly referred to as approximate function memoization 15,20,30 . The only difference between these two approaches is in its matching rule: the former searches for an exact match, and the latter search for an inexact (approximate) match.…”

Section: Preliminaries and Discussionmentioning

confidence: 99%

“…21 Figure 5B shows the second scenario, which is commonly referred to as approximate function memoization. 15,20,30 The only difference…”

Section: Memoization Scenariosmentioning

confidence: 99%

“…Literature 6 provides a clear distinction between them. But when we compare CR [7][8][9][10][11][12][13][14][15][16]30 and MZ, [17][18][19][20][21][22][23][24][25][26][27][28][29]35,36 we could not found sufficient difference among these two. Conceptually, CR refers to "reusing previously computed (buffered) results if same input appears again," and MZ refers to "storing the current result and reuse in future if similar input appears again.…”

Section: Prior Workmentioning

confidence: 99%

“…We categorize these works into three groups based on either a hardware or a software approach, as shown in Figure 1. Value prediction 2‐5 is a hardware approaches, but computation reuse 6‐16 and memoization 17‐30 can be implemented using hardware as well as software. A standard procedure to execute these techniques follows some simple steps described below: Analyze and identify the hot code (hc) of a program to concentrate your optimization effort.…”

Section: Introductionmentioning

confidence: 99%

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Approximate function memoization

Arundhati

Jena

Pani

2022

Concurrency and Computation

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Summary Function memoization is an optimization technique that reduces a function call overhead when the same input appears again. A table that stores the previous result is searched and used to skip the repeated computation. This way, it increases the performance of the function call. In this article, we propose a software approach of function memoization to improve computing efficiency by bypassing the execution of the function implemented using approximate computing techniques. Searching overhead is a primary concern in any memoization technique proposed so far. In traditional function memoization, the input arguments are first searched in the look‐up table (LUT) for an exact match, and the corresponding result is extracted for further use. But, in this article, a decision‐making rule is proposed to help us decide whether to search the LUT or go for the actual computation. This decision‐making model is implemented through Bloom filter and Cantor's pairing function. Because Bloom filter sometimes produces false‐positive results, we suggest a simple approximation technique that searches the LUT for an approximate match rather than an exact match. The proposed model also contains a bypass algorithm implemented through C++ code that identifies the trivial computations from the input argument of the candidate function. By this, we can avoid the actual calculation and generate the result directly. Here, trivial computation identifies one or more input arguments that are either 0 or prefix±1$$ \pm 1 $$. To analyze the effectiveness of our proposed technique, we conducted several experiments using the benchmarks from the AxBench suite. We found that our result outperforms some of the methods proposed so far in terms of energy consumption and quality of results, particularly in image processing applications.

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Section: Preliminaries and Discussionmentioning

confidence: 99%

“…21 Figure 5B shows the second scenario, which is commonly referred to as approximate function memoization. 15,20,30 The only difference…”

Section: Memoization Scenariosmentioning

confidence: 99%

Section: Prior Workmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Approximate function memoization

Arundhati

Jena

Pani

2022

Concurrency and Computation

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“…Memoization samples patterns of computations to identify and return possible similar outputs previously computed and stored [102]. Further exclusive software and hardware techniques, there are co-approximations between hardware and software that enable control by software to hardware components used only on the approximation, like on quantized lookup tables controlled by software procedures [52,91].…”

Section: Software Approximationmentioning

confidence: 99%

Software and hardware interfaces for approximate memories

Fabrício Filho

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I would like to thank the many people that give me support during this Ph.D.To my wife, Aline Maria Gonçalves Fabrício, for walking alongside me on this journey giving me support, and making me happy every day.To my advisor Lucas Wanner, for all the mentoring and guidance that were fundamental in formulating research questions and in my professional development.To Isaías Bittencourt Felzmann, for the feedback, ideas, collaborations on manuscripts, and the several discussions about the topics of this research.To Rodolfo Azevedo, for the support that improves the contributions of this work.

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Approximation Opportunities in Edge Computing Hardware: A Systematic Literature Review

Damsgaard¹,

Ometov²,

Nurmi

2023

ACM Comput. Surv.

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With the increasing popularity of the Internet of Things and massive Machine Type Communication technologies, the number of connected devices is rising. However, while enabling valuable effects to our lives, bandwidth and latency constraints challenge Cloud processing of their associated data amounts. A promising solution to these challenges is the combination of Edge and approximate computing techniques that allows for data processing nearer to the user. This paper aims to survey the potential benefits of these paradigms’ intersection. We provide a state-of-the-art review of circuit-level and architecture-level hardware techniques and popular applications. We also outline essential future research directions.

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Data clustering for efficient approximate computing

Cited by 4 publications

References 32 publications

Approximate function memoization

Approximate function memoization

Software and hardware interfaces for approximate memories

Approximation Opportunities in Edge Computing Hardware: A Systematic Literature Review

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