Approximeter: Automatically Finding and Quantifying Code Sections for Approximation
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© 2017 IEEE. Approximate computing is getting a lot of traction especially for its potential in improving power, performance, and scalability of a computing system. However, prior work heavily relies upon a programmer to identify code sections where various approximation techniques can be applied. Such an approach is error prone and cannot scale well beyond small applications. In this paper, we contribute with a tool, called Approximeter, to automatically identify and quantify code sections where approximation can be used and to what extant. The tool works by first identifying potential approximable functions and then, injecting errors at appropriate locations. The tool runs Monte Carlo experiments to quantify statistical relation between injected error and corresponding output accuracy. The tool also provides a rough estimate of potential performance gain from approximating a certain function. Finally, it ranks the approximable functions based on their error tolerance and performance gain.
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