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This paper introduces CoTM, a coherence-aware task mapping framework that enhances communication efficiency in many-core Network-on-Chip (NoC) systems by inferring inter-task dependencies from dynamic coherence behavior. By addressing the limitations of existing task mapping methods that overlook cache coherence and rely on predefined task graphs, CoTM employs a multi-start optimization strategy with a coherence-aware penalty function to refine task placement. Experimental results show that CoTM significantly reduces average link utilization by up to 47.85% and total energy consumption by up to 10.30%, underscoring the critical role of cache coherence in optimizing task mapping.
Ignoring cache coherence in task mapping can lead to a staggering 47.85% increase in link utilization and energy waste鈥擟oTM flips this narrative.
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.
