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Optimizing ML Workload Partitioning between CPUs and CIM Accelerators for Heterogeneous Computing
and-cluster-computingartificial-intelligencedistributedemerging-technologieshardware-architecturemachine-learningparallel
Computing-in-Memory (CIM) accelerators execute Matrix-Vector Multiplications (MVMs) in memory, making them a compelling solution for Machine Learning (ML) workloads. However, existing ML workload partitioning approaches for CIM accelerators do not fully account for Resistive Random Access Memory (RR
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