Abstract: Training machine learning models often involves solving high-dimensional stochastic optimization problems, where stochastic gradient-based algorithms are hindered by slow convergence.
Abstract: As artificial intelligence (AI) and computational models grow in scale, the demand for computational power and storage has significantly increased. The computing-in-memory (CIM) architecture ...
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