Presentation
CIMAP: A CIM Crossbar Array Data Mapping Methodology for Unstructured Sparse Convolutional Neural Networks
DescriptionThe irregularly distributed zeros in pruned CNN networks will still be loaded and processed by CIM, which causes inefficient usage of memory arrays. We propose CIMAP, a CIM data mapping methodology for unstructured sparse CNN. CIMAP flexibly rearranges non-zero weights in unstructured sparse CNN models by strategically swapping rows within the weight map. CIMAP also introduces a CIM processor with switchable CIM macros to efficiently handle sparse weight maps. The experimental results have shown that CIMAP attains up to 1.97x, 1.8x better performance, and 7.45x, 5.02x energy efficiency improvement than the conventional CIM solutions for unstructured sparse VGG16 and ResNet50.
Event Type
Work-in-Progress Poster
TimeWednesday, June 265:00pm - 6:00pm PDT
LocationLevel 2 Lobby
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