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arXiv · 2510.24113

Taming the Tail: NoI Topology Synthesis for Mixed DL Workloads on Chiplet-Based Accelerators

Arnav Shukla·Harsh Sharma·Srikant Bharadwaj·Vinayak Abrol·Sujay Deb
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Abstract

Heterogeneous chiplet-based systems improve scaling by disag-gregating CPUs/GPUs and emerging technologies (HBM/DRAM).However this on-package disaggregation introduces a latency inNetwork-on-Interposer(NoI). We observe that in modern large-modelinference, parameters and activations routinely move backand forth from HBM/DRAM, injecting large, bursty flows into theinterposer. These memory-driven transfers inflate tail latency andviolate Service Level Agreements (SLAs) across k-ary n-cube base-line NoI topologies. To address this gap we introduce an InterferenceScore (IS) that quantifies worst-case slowdown under contention.We then formulate NoI synthesis as a multi-objective optimization(MOO) problem. We develop PARL (Partition-Aware ReinforcementLearner), a topology generator that balances throughput, latency,and power. PARL-generated topologies reduce contention at the memory cut, meet SLAs, and cut worst-case slowdown to 1.2 times while maintaining competitive mean throughput relative to link-rich meshes. Overall, this reframes NoI design for heterogeneouschiplet accelerators with workload-aware objectives.

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Arnav Shukla, Harsh Sharma, Srikant Bharadwaj, Vinayak Abrol, Sujay Deb. 2025-10-28. Taming the Tail: NoI Topology Synthesis for Mixed DL Workloads on Chiplet-Based Accelerators. https://arxiv.org/abs/2510.24113

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