用机器学习简化符号加速器中的自动机，降低资源占用。
ML-Based Automata Simplification for Symbolic Accelerators
- 基于随机森林分析边权与结构特征，智能剪除低效转移路径。
- 在1K到64K节点图上实现超30%转移边剪枝，最多节省40%LUT资源。
- 适合需部署于FPGA的符号处理系统优化，尤其在基因组等大数据场景。
符号加速器在基因组学、自然语言处理和网络安全等领域广泛用于符号数据处理，但其可扩展性受限于内存消耗和路由复杂度，尤其是在大规模集合下。本文提出AutoSlim，一种基于机器学习的图简化框架，用于降低基于非确定有限自动机（NFA）的符号加速器在FPGA叠层（如NAPOLY+）上的复杂度。AutoSlim采用随机森林分类器，根据边评分和结构特征剪除低影响转移，显著降低自动机图密度，同时保持语义正确性。相比以往工具，AutoSlim支持带权转移的自动化评分感知简化，实现高效基于排序的序列分析。我们在包含1K至64K节点的数据集上评估了NAPOLY+的性能，测量了延迟、吞吐量和资源使用情况。结果表明，AutoSlim实现了高达40%的FPGA LUT减少和超过30%的转移边剪枝，并能处理比现有基准大一个数量级的图。此外，实验显示硬件互连（扇出）对硬件成本影响显著，而AutoSlim的剪枝有效缓解了资源激增问题。
原文摘要 · Abstract (English)
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.
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