REASON: 加速概率逻辑推理以实现可扩展的神经符号智能
REASON: Accelerating Probabilistic Logical Reasoning for Scalable Neuro-Symbolic Intelligence
AI总结：
REASON通过统一图表示和自适应剪枝技术，实现概率逻辑推理的加速，提升神经符号AI的效率与可扩展性。
摘要：
神经符号AI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、可解释和鲁棒的智能。尽管这种组合范式在推理、规划和验证等领域表现出色，但其部署仍然具有挑战性，因为符号和概率推理存在严重的效率问题。通过系统分析代表性神经符号工作负载，我们发现概率逻辑推理是效率瓶颈，其特征是不规则的控制流、低算术强度、非聚合同步的内存访问以及在CPU和GPU上的硬件利用率低。本文提出了REASON，一个用于神经符号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，该表示捕捉了符号和概率模型之间的共同结构，并结合了自适应剪枝和正则化。在架构层面，REASON具有可重新配置的、基于树的处理织构，优化于不规则遍历、符号推导和概率聚合。在系统层面，REASON通过可编程接口和多级流水线紧密集成到GPU流多处理器中，高效地协调组合执行。在六个神经符号工作负载上进行评估，REASON在台式机和边缘GPU上实现了12-50倍的速度提升和310-681倍的能量效率。REASON实现了实时的概率逻辑推理，以0.8秒完成端到端任务，使用6mm²面积和2.12W功率，证明了针对概率逻辑推理的针对性加速对于实际和可扩展的神经符号AI至关重要，并将REASON定位为下一代认知智能的基础系统架构。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
