提出KLay结构，让神经符号AI的计算电路在GPU上快得多
KLay: Accelerating Arithmetic Circuits for Neurosymbolic AI
- 用新数据结构KLay表示逻辑电路，支持GPU高效并行
- 实测速度比现有方法快多个数量级
- 适合需要大规模推理的神经符号系统开发者
神经符号AI常将逻辑公式映射为算术电路（由加法和乘法构成的计算图），并将神经网络输出输入其中，以可微方式施加符号约束。然而，算术电路在现代AI加速器上运行困难，因其存在高度不规则的稀疏性。为此，本文提出知识层（KLay）——一种可高效并行化于GPU的算术电路表示新结构。我们还设计了两种算法，用于将传统电路表示转换为KLay，以及一个在电路求值时挖掘并行机会的算法。实验表明，KLay相比现有技术实现多个数量级的速度提升，为神经符号AI扩展至更大规模实际应用铺平道路。
原文摘要 · Abstract (English)
A popular approach to neurosymbolic AI involves mapping logic formulas to arithmetic circuits (computation graphs consisting of sums and products) and passing the outputs of a neural network through these circuits. This approach enforces symbolic constraints onto a neural network in a principled and end-to-end differentiable way. Unfortunately, arithmetic circuits are challenging to run on modern AI accelerators as they exhibit a high degree of irregular sparsity. To address this limitation, we introduce knowledge layers (KLay), a new data structure to represent arithmetic circuits that can be efficiently parallelized on GPUs. Moreover, we contribute two algorithms used in the translation of traditional circuit representations to KLay and a further algorithm that exploits parallelization opportunities during circuit evaluations. We empirically show that KLay achieves speedups of multiple orders of magnitude over the state of the art, thereby paving the way towards scaling neurosymbolic AI to larger real-world applications.
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