BDD-Based Error Metric Analysis, Computation and Optimization

Approximate Computing is a design paradigm that trades off computational accuracy for gains in non-functional aspects such as reduced area, increased computation speed, or power reduction. Computing the error of the approximated design is an essential step to determine its quality. The computation time for determining the error can become very large, effectively rendering the entire logic approximation procedure infeasible. In this work we extensively analyze various error metrics and approximation operations. We present methods to accelerate the computation of error metric computations by 1) exploiting structural information of the function obtained by applying the analyzed operations and 2) computing estimates of the metrics for multi-output Boolean functions represented as Binary Decision Diagrams (BDDs). We further present a novel greedy, bucket-based BDD minimization framework employing the newly proposed error metric computations to produce Pareto-optimal solutions with respect to BDD size and multiple error metrics. The applicability of the proposed minimization framework is demonstrated by an experimental evaluation. We can report considerable speedups while, at the same time, creating high-quality approximated BDDs. The presented framework is publicly available as open-source software on GitHub.