XMeter: Finding Approximable Functions and Predicting Their Accuracy

doi:10.1109/TC.2020.3005083

2021-07-01

Riad Akram

Shantanu Mandal

Abdullah Muzahid *

DOI：10.1109/TC.2020.3005083

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摘要

En 中文

Approximate computing has significant potential to improve the efficiency of a computing system. Numerous techniques have been proposed in literature. Virtually, all of them require programmers to either experiment with every instance of a specific type of code region exhaustively to find approximable code regions or annotate such regions manually. Both approaches are error-prone and can lead to missed opportunities. Therefore, we propose XMeter to automatically find and quantify approximable code regions. XMeter, first, analyzes the application code statically using a novel algorithm based on memory location updates. Also, XMeter provides a deep learning-based predictor to predict the accuracy of the application when different code regions are approximated. Our proposed scheme does not require the programmer to experiment exhaustively for all possible error rates and types of approximation techniques. Moreover, the scheme does not require any domain knowledge and is not specific to any approximation technique. Therefore, it is general enough to be applicable for any approximation technique. We developed XMeter using LLVM and experimented with 10 applications. We analyzed 43 approximable functions and found 21 to be highly tolerant of errors. We validated our results using 4 well-known approximation techniques and showed that XMeter can predict an application's accuracy accurately.

Keyword:

Error analysis

Static analysis

Hardware

Neural networks

Approximation algorithms

Prediction algorithms

Machine learning

Approximate computing

accuracy

static analysis

machine learning