RiskCap: Minimizing Effort of Error Regulation for Approximate Computing
Shuhao Jiang, Jiajun Li, Xin James He, Guihai Yan, Xuan Zhang, Xiaowei Li · 2018
Quality management, which is responsible for controlling approximation quality to meet user requirement, plays a key role in the applicability of approximate computing. An effective and efficient quality management needs to be accurate to detect intolerable errors meanwhile light-weight in nature. However, it is difficult to design such a quality management satisfying both the two demands and existing work usually optimizes for one demand at the expense of the other. In this paper, we aim to achieve higher energy efficiency of quality management by optimizing detection accuracy and overhead simultaneously. We observe that the detection difficulty varies across inputs and there exists much redundant computation in detection process. Based on this observation, a cascaded quality management which can minimize the overhead and doesn't lower detection accuracy is proposed. The proposed solution pays more proper computation effort according to different detection difficulties of inputs so as to avoid unnecessary energy consumption. What's more, by exploring the design space sufficiently and effectively, we can assure the highest energy-efficiency of the proposed topology. The experiment results demonstrate that our approach can achieve much greater energy-efficiency than existing solutions.