Systematic Design of Approximate Adder Using Significance Based Gate-Level Pruning (SGLP) for Image Processing Application

Jena, Sisir Kumar; Biswas, Santosh; Deka, Jatindra Kumar

doi:10.1007/978-3-030-34872-4_62

Cited by 4 publications

(4 citation statements)

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“…Now, let us talk about approximate computing (AxC) essentials. AxC refers to the methodologies that help us achieve performance with a loss in the accuracy of the result 34,37 …”

Section: Prior Workmentioning

confidence: 99%

“…AxC refers to the methodologies that help us achieve performance with a loss in the accuracy of the result. 34,37 It is evident to discuss all previously published approximate schemes on function memoization here. The approximate scheme's primary focus is to provide a better solution to the issues of function memoization described in the introduction section.…”

Section: Prior Workmentioning

confidence: 99%

“…Recently, function memoization went through several modifications to improve execution time, performance, and energy efficiency 24‐29,33 . One such technique is approximate computing (AxC)—an alternative method to exact computing that utilizes the error‐resilience property of many applications and helps in reducing the power and area consumption with an expense of loss in accuracy of the result 34 . In this article, we propose a software‐based approximate function memoization technique that uses the concept of AxC to improve processor performance.…”

Section: Introductionmentioning

confidence: 99%

“…[24][25][26][27][28][29]33 One such technique is approximate computing (AxC)-an alternative method to exact computing that utilizes the error-resilience property of many applications and helps in reducing the power and area consumption with an expense of loss in accuracy of the result. 34 In this article, we propose a software-based approximate function memoization technique that uses the concept of AxC to improve processor performance. The significant issues of function memoization (irrespective of any technology used so far) are:…”

mentioning

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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“…Now, let us talk about approximate computing (AxC) essentials. AxC refers to the methodologies that help us achieve performance with a loss in the accuracy of the result 34,37 …”

Section: Prior Workmentioning

confidence: 99%

Section: Prior Workmentioning

confidence: 99%