Space Expansion of Feature Selection for Designing more Accurate Error Predictors Shayan Tabatabaei Nikkhah Mehdi Kamal Ali Afzali-Kusha Department of Electrical School of Electrical and Computer School of Electrical and Computer Engineering Engineering Engineering Eindhoven University of University of Tehran University of Tehran Technology Tehran, Iran Tehran, Iran Eindhoven, The Netherlands
[email protected] [email protected] [email protected] Massoud Pedram Department of Electrical Engineering University of Southern California Los Angeles, CA, USA
[email protected] ABSTRACT outputs. These errors strongly depend on application inputs [6], putting an extra emphasis on quality control in approximate Approximate computing is being considered as a promising computing. One of the proposed solutions for managing the design paradigm to overcome the energy and performance output quality is sampling the output elements and changing the challenges in computationally demanding applications. If the case approximate configuration accordingly to meet the quality where the accuracy can be configured, the quality level versus requirements [3], [7]. Given that output errors are strongly energy efficiency or delay also may be traded-off. For this dependent to the inputs, this approach is highly susceptible to technique to be used, one needs to make sure a satisfactory user missing the output elements with large errors [6]. experience. This requires employing error predictors to detect There is another approach emphasized in recent works (e.g., unacceptable approximation errors. In this work, we propose a [6], [8], [9]) which leverages light-weight quality checkers to scheduling-aware feature selection method which leverages the dynamically manage the output quality.