AutoSlim uses a Random Forest model trained on prior execution features to prune redundant parts of automata graphs, reducing FPGA resources by up to 40% in symbolic accelerators with a verification check for equivalence.
Automata processor applications in bioinformatics: A survey
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Machine Learning-Based Graph Simplification for Symbolic Accelerators
AutoSlim uses a Random Forest model trained on prior execution features to prune redundant parts of automata graphs, reducing FPGA resources by up to 40% in symbolic accelerators with a verification check for equivalence.