AI RESEARCH
Machine Learning-Based Graph Simplification for Symbolic Accelerators
arXiv CS.LG
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ArXi:2605.08996v1 Announce Type: new Graph-based accelerators have been widely adopted in symbolic data processing applications such as genomics, cybersecurity, and artificial intelligence. However, these systems often suffer from excessive memory usage and inefficiencies stemming from redundant graph structures. We present AutoSlim, a machine learning-based framework that leverages data-driven methods to prune automata graphs for hardware accelerators.