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PolyCIM: Improving Data Reuse in Digital CIM Accelerators with Polyhedral-Based Compilation
Digital Compute-in-Memory (CIM) presents a promising solution for accelerating deep neural networks (DNNs) through the integration of computational logic directly within memory arrays. However, mapping modern DNN operators to CIM accelerators often results in severe array underutilization, due to th
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