AI RESEARCH
Estimation of Energy-dissipation Lower-bounds for Neuromorphic Learning-in-memory
arXiv CS.AI
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ArXi:2402.14878v4 Announce Type: replace-cross Neuromorphic or neurally-inspired optimizers rely on local but parallel parameter updates to solve problems that range from quadratic programming to Ising machines. An ideal realization of such an optimizer not only uses a compute-in-memory (CIM) paradigm to address the so-called memory-wall (i.e.