Functional Approximation and Approximate Parallelization with the ACCEPT compiler

Approximate computing can aid in the use of energy on cloud and mobile systems by exchanging result accuracy for faster processing times and energy efficiency. One fundamental challenge in applying approximate techniques arises when trying to identify which parts of the application are resilient to approximations, since resilience is not fixed and is dependent on the sensibility of the code. ACCEPT [1] is an approximate computing framework that applies multiple approximation techniques at the compiling level, and that presents suggestions of sections of code that could be annotated to enforce such techniques. The original ACCEPT framework applies a variety of approximation techniques, including hardware acceleration and loop perforation. This work extends ACCEPT in order to attack a broader range of applications and approximation techniques. We extend the framework to support function approximation and approximate loop parallelization. We evaluate the extended framework with seven benchmarks, showing the resulting speedup and quality degradation for each technique when applied in isolation and in combination with other approximation targets. We introduce these approximations without requiring application-level annotations. Our experiments with set benchmarks showed maximum speedups ranging from 1.22x up to 634x for the combined techniques, with quality degradation between 0% and 29%.