3 papers
cs.AR2025
MDM: Manhattan Distance Mapping of DNN Weights for Parasitic-Resistance-Resilient Memristive Crossbars
Matheus Farias, Wanghley Martins, H. T. Kung
Manhattan Distance Mapping (MDM) is a post-training deep neural network (DNN) weight mapping technique for memristive bit-sliced compute-in-memory (CIM) crossbars that reduces para…
cs.AR2024
Efficient Reprogramming of Memristive Crossbars for DNNs: Weight Sorting and Bit Stucking
Matheus Farias, H. T. Kung
We introduce a novel approach to reduce the number of times required for reprogramming memristors on bit-sliced compute-in-memory crossbars for deep neural networks (DNNs). Our ide…
cs.AR2024
Sorted Weight Sectioning for Energy-Efficient Unstructured Sparse DNNs on Compute-in-Memory Crossbars
Matheus Farias, H. T. Kung
We introduce (SWS): a weight allocation algorithm that places sorted deep neural network (DNN) weight sections on bit-sliced compute-in-memory (…