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cs.ET2026
Optimizing ML Workload Partitioning between CPUs and CIM Accelerators for Heterogeneous Computing
Joel Klein, Rebecca Pelke, Roberto Laudani +2
Computing-in-Memory (CIM) accelerators execute Matrix-Vector Multiplications (MVMs) in memory, making them a compelling solution for Machine Learning (ML) workloads. However, exist…
cs.LG2026
Mixed-Precision Training and Compilation for RRAM-based Computing-in-Memory Accelerators
Rebecca Pelke, Joel Klein, Jose Cubero-Cascante +3
Computing-in-Memory (CIM) accelerators are a promising solution for accelerating Machine Learning (ML) workloads, as they perform Matrix-Vector Multiplications (MVMs) on crossbar a…