A fast BDD Minimization Framework for Approximate Computing
Wendler A, Keszöcze O (2020)
Publication Language: English
Publication Type: Conference contribution, Original article
Publication year: 2020
Event location: ALPEXPO, Grenoble, France
Abstract
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.
As a remedy, we present methods to accelerate the computation of error metric computations by (a) exploiting structural information and (b) computing estimates of the metrics for multi-output Boolean functions represented as 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.
Authors with CRIS profile
Related research project(s)
None - None
ACoF -- Approximate Computing on FPGAs (ACoF)
June 1, 2021 - June 1, 2024
Preparation of DFG funding proposal: ACoF (ACoF)
Jan. 15, 2019 - April 14, 2020
How to cite
APA:
Wendler, A., & Keszöcze, O. (2020). A fast BDD Minimization Framework for Approximate Computing. In Proceedings of the Design, Automation and Test in Europe. ALPEXPO, Grenoble, France, FR.
MLA:
Wendler, Andreas, and Oliver Keszöcze. "A fast BDD Minimization Framework for Approximate Computing." Proceedings of the Design, Automation and Test in Europe, ALPEXPO, Grenoble, France 2020.
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