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To address increasing compute demand from recent multi-model workloads withheavy models like large language models, we propose to deploy heterogeneouschiplet-based multi-chip module (MCM)-based accelerators. We develop anadvanced scheduling framework for heterogeneous MCM accelerators thatcomprehensively consider complex heterogeneity and inter-chiplet pipelining.Our experiments using our framework on GPT-2 and ResNet-50 models on a4-chiplet system have shown upto 2.2x and 1.9x increase in throughput andenergy efficiency, compared to a monolithic accelerator with an optimizedoutput-stationary dataflow.

Inter-Layer Scheduling Space Exploration for Multi-model Inference on Heterogeneous Chiplets