AI RESEARCH
Optimizing Binary and Ternary Neural Network Inference on RRAM Crossbars using CIM-Explorer
arXiv CS.LG
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ArXi:2505.14303v2 Announce Type: replace-cross Using Resistive Random Access Memory (RRAM) crossbars in Computing-in-Memory (CIM) architectures offers a promising solution to overcome the von Neumann bottleneck. Due to non-idealities like cell variability, RRAM crossbars are often operated in binary mode, utilizing only two states: Low Resistive State (LRS) and High Resistive State (HRS). Binary Neural Networks (BNNs) and Ternary Neural Networks (TNNs) are well-suited for this hardware due to their efficient mapping.