ML-Based Automata Simplification for Symbolic Accelerators
This addresses scalability problems for developers using symbolic accelerators in domains like genomics, NLP, and cybersecurity, though it appears incremental as it builds on existing FPGA overlays like NAPOLY+.
The paper tackles scalability issues in symbolic accelerators by presenting AutoSlim, a machine learning-based framework that simplifies Non-deterministic Finite Automata graphs, achieving up to 40% reduction in FPGA LUTs and over 30% pruning in transitions while scaling to larger graphs.
Symbolic accelerators are increasingly used for symbolic data processing in domains such as genomics, NLP, and cybersecurity. However, these accelerators face scalability issues due to excessive memory use and routing complexity, especially when targeting a large set. We present AutoSlim, a machine learning-based graph simplification framework designed to reduce the complexity of symbolic accelerators built on Non-deterministic Finite Automata (NFA) deployed on FPGA-based overlays such as NAPOLY+. AutoSlim uses Random Forest classification to prune low-impact transitions based on edge scores and structural features, significantly reducing automata graph density while preserving semantic correctness. Unlike prior tools, AutoSlim targets automated score-aware simplification with weighted transitions, enabling efficient ranking-based sequence analysis. We evaluated data sets (1K to 64K nodes) in NAPOLY+ and conducted performance measurements including latency, throughput, and resource usage. AutoSlim achieves up to 40 percent reduction in FPGA LUTs and over 30 percent pruning in transitions, while scaling to graphs an order of magnitude larger than existing benchmarks. Our results also demonstrate how hardware interconnection (fanout) heavily influences hardware cost and that AutoSlim's pruning mitigates resource blowup.