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An Emerging NVM-Based On-Chip Training Architecture with Non-Ideality Mitigation Through Bipolar Weight Distributions
The rapid advancement of deep learning has presented significant energy efficiency challenges to the conventional von Neumann architecture. In-memory computing (IMC) architectures based on emerging non-volatile memory (eNVM) are widely regarded as a promising solution for accelerating neural network
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