AI 中文总结
针对现有任务映射方法未考虑缓存一致性及任务图表示不全面的问题，提出CoTM框架，通过推断任务依赖构建任务图，结合轻量级启发式算法和多起点优化策略，有效降低链路利用率和能耗。
AI 中文摘要
在基于片上网络（NoC）的多核系统中，缓存一致性对于通信至关重要。随着应用规模和复杂度增加，有效管理通信愈发具有挑战性，任务映射成为关键优化技术。现有任务映射方法存在两大局限：依赖预定义任务图，未明确捕捉共享数据访问产生的一致性诱导交互，只能部分表示任务间关系；通常忽略缓存一致性。为此提出CoTM，一种通过从动态一致性行为推断任务间依赖来构建任务图的一致性感知任务映射框架。采用轻量级启发式算法和多起点优化策略，由考虑一致性流量和NoC性能指标的惩罚函数引导迭代优化任务放置。实验结果表明，与现有方法相比，CoTM可将平均链路利用率降低高达47.85%，总能耗降低高达10.30%。凸显将缓存一致性纳入任务映射的重要性及一致性感知优化对未来多核NoC系统的潜力。
英文摘要
Cache coherence is essential for communication in many-core Network-on-Chip (NoC)-based systems. As application scale and complexity increase, efficiently managing communication becomes increasingly challenging, making task mapping a key optimization technique. However, existing task mapping approaches suffer from two major limitations. First, they rely on predefined task graphs whose dependencies are typically derived from program structure or runtime information, such as dataflow, synchronization, traces, or profiling, without explicitly capturing coherence-induced interactions arising from shared data accesses. Consequently, these graphs provide only a partial representation of inter-task relationships, limiting mapping effectiveness. Second, they generally overlook cache coherence, even though coherence traffic constitutes a significant portion of NoC communication. This mismatch between modeled communication behavior and actual runtime interactions often leads to suboptimal mappings and degraded system performance. To address these limitations, we propose CoTM, a coherence-aware task mapping framework that constructs task graphs by inferring inter-task dependencies from dynamic coherence behavior. CoTM employs a lightweight heuristic with a multi-start optimization strategy to iteratively refine task placement, guided by a coherence-aware penalty function that jointly considers coherence traffic and NoC performance metrics. Experimental results demonstrate that CoTM reduces average link utilization by up to 47.85% and total energy consumption by up to 10.30% compared with existing approaches. These results highlight the importance of incorporating cache coherence into task mapping and demonstrate the potential of coherence-aware optimization for future many-core NoC systems.
CommentsAccepted by ICCAD 2026
