Neuro-symbolic AI systems integrate neural perception with symbolic reasoning to enable data-efficient, interpretable, and robust intelligence beyond purely neural models. Although this compositional paradigm has shown superior performance in domains such as reasoning, planning, and verification, its deployment remains challenging due to severe inefficiencies in symbolic and probabilistic inference. Through systematic analysis of representative neuro-symbolic workloads, we identify probabilistic logical reasoning as the inefficiency bottleneck, characterized by irregular control flow, low arithmetic intensity, uncoalesced memory accesses, and poor hardware utilization on CPUs and GPUs. This paper presents REASON, an integrated acceleration framework for probabilistic logical reasoning in neuro-symbolic AI. REASON introduces a unified directed acyclic graph representation that captures common structure across symbolic and probabilistic models, coupled with adaptive pruning and regularization. At the architecture level, REASON features a reconfigurable, tree-based processing fabric optimized for irregular traversal, symbolic deduction, and probabilistic aggregation. At the system level, REASON is tightly integrated with GPU streaming multiprocessors through a programmable interface and multi-level pipeline that efficiently orchestrates compositional execution. Evaluated across six neuro-symbolic workloads, REASON achieves 12-50x speedup and 310-681x energy efficiency over desktop and edge GPUs under TSMC 28 nm node. REASON enables real-time probabilistic logical reasoning, completing end-to-end tasks in 0.8 s with 6 mm2 area and 2.12 W power, demonstrating that targeted acceleration of probabilistic logical reasoning is critical for practical and scalable neuro-symbolic AI and positioning REASON as a foundational system architecture for next-generation cognitive intelligence.

翻译：神经符号人工智能系统将神经感知与符号推理相结合，以实现超越纯神经模型的数据高效、可解释且鲁棒的智能。尽管这种组合范式在推理、规划和验证等领域已展现出卓越性能，但由于符号与概率推断存在严重的效率低下问题，其实际部署仍面临挑战。通过对代表性神经符号工作负载的系统分析，我们确定概率逻辑推理是效率瓶颈所在，其特点在于不规则的控制流、低算术强度、非合并的内存访问以及在CPU和GPU上较差的硬件利用率。本文提出REASON，一个面向神经符号AI中概率逻辑推理的集成加速框架。REASON引入了一种统一的有向无环图表示方法，以捕捉符号与概率模型间的共同结构，并结合自适应剪枝与正则化技术。在架构层面，REASON采用一种可重构的、基于树的处理结构，针对不规则遍历、符号演绎和概率聚合进行了优化。在系统层面，REASON通过可编程接口和多级流水线紧密集成于GPU流式多处理器，高效编排组合式执行。在六种神经符号工作负载上的评估表明，在台积电28纳米工艺节点下，REASON相比桌面及边缘GPU实现了12-50倍的加速和310-681倍的能效提升。REASON能够实现实时概率逻辑推理，在6 mm²面积和2.12 W功耗下，端到端任务可在0.8秒内完成。这证明针对概率逻辑推理的定向加速对于实用且可扩展的神经符号AI至关重要，并使REASON成为下一代认知智能的基础系统架构。