AI RESEARCH
Robust Reasoning and Learning with Brain-Inspired Representations under Hardware-Induced Nonlinearities
arXiv CS.LG
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ArXi:2604.12079v1 Announce Type: cross Traditional machine learning depends on high-precision arithmetic and near-ideal hardware assumptions, which is increasingly challenged by variability in aggressively scaled semiconductor devices. Compute-in-memory (CIM) architectures alleviate data-movement bottlenecks and improve energy efficiency yet