The emergence of chiplet-based architectures represents a paradigm shift in post-Moore’s Law computing systems, offering substantial cost and yield advantages through functional disaggregation. However, the heterogeneity of inter-chiplet communication introduces unique performance challenges that conventional partitioning strategies fail to address. In this work, the ways in which poor workload partitioning degrades communication performance in chiplet-based systems are comprehensively characterized. We demonstrate, through a detailed experimental analysis, that suboptimal workload partitioning can increase inter-chiplet communication latency by up to a factor of 10 and inflate network congestion beyond sustainable levels as systems scale. Our findings show that optimized partitioning strategies can achieve an 87.4% reduction in inter-chiplet traffic, improve system throughput by a factor of 8.75, and enhance energy efficiency by a factor of 10.3 compared to naive partitioning approaches. We further characterize how these effects scale with system size, revealing that the communication overhead can consume 85% of the execution time in poorly partitioned 16-chiplet systems, compared to only 35% in well-partitioned configurations. This work provides essential insights into the communication-aware design space of chiplet systems and validates the critical importance of sophisticated workload partitioning algorithms.
Mbua et al. (2026) studied this question.