Effects of Approximate Computing on Workload Characteristics

Abstract

In recent years, many new approaches in approximate computing have been presented. These techniques have shown great opportunities to improve performance or energy consumption of applications while trading often a negligible decrease in accuracy. Many different techniques have been invented, ranging from hardware techniques to approaches purely implemented in software, compilers, and frameworks. Research has shown that applications often have very specific demands on hardware and usually suffer from specific bottlenecks such as memory bandwidth or compute capabilities. Developers optimize applications employing advanced techniques to optimally exploit hardware capabilities. We study how the workload character of the applications is affected when they are optimized using approximate computing techniques. We analyze the detailed micro-architectural application character using 55 characteristics. We evaluate four approximate computing techniques on all 37 applications from PolyBench/C and AxBench. We show how the optimization using the different approximation techniques influences both the properties of individual applications and groups of similar applications’ characters. We find results that contradict general expectations, such as an increasing number of instructions executed when the opposite is expected. Furthermore, some applications are slowed down when approximated, which is confirmed by the number of executed instructions. These results show that the approximation of an application changes the core of its characteristics and that a detailed analysis of approximated applications is required. Interference between traditional optimizations and approximation requires a holistic approach.