Mapping Without Graphs: Learning Coherence Traffic for Task Placement
The problem is that existing task mapping approaches rely on predefined task graphs that fail to capture coherence-induced interactions from shared data accesses, leading to suboptimal mappings. CoTM addresses this by constructing task graphs inferred from dynamic coherence behavior and using a lightweight heuristic with a multi-start optimization strategy guided by a coherence-aware penalty function. Experimental results show CoTM reduces average link utilization by up to 47.85% and total energy consumption by up to 10.30% compared to existing approaches. This matters because it demonstrates that incorporating cache coherence into task mapping significantly improves performance and energy efficiency for future many-core NoC systems.