ACR: Enabling computation reuse for approximate computing
Xin James He, Guihai Yan, Yinhe Han, Xiaowei Li · 2016
Approximate computing, which trades off computation quality (e.g, accuracy) and computation efforts, has becoming a promising technique to improve performance for many mission-non-critical and error-tolerant applications. The computations in such applications usually exhibit superior value locality, i.e, computations performed by a function or code region are very likely to reproduce “similar” results. Reusing the similar results can bypass redundant computations, as long as “exact” results are not mandatory. However, conventional computation reuse techniques are less effective in approximate computing paradigm. The input values of two computation instances have to be identical to reuse one for another, hence “exact” in nature.We propose ACR, an approximate computation reuse framework, to enable computation reuse for approximate computing. ACR relaxes the exact matching requirement in inputs to some extent regulated by “similarity” quantification, thereby shifting the exact computation reuse paradigm to its approximate counterpart. We furthermore propose an input significance-aware similarity quantification scheme through statistical approaches. Experimental result shows ACR could effectively exploit the potential of computation reuse for approximate computing and reduce 47.6% computations on average for a set of approximate applications.