The growing demand for high-precision neural network training and inference has driven the necessity for floating-point (FP) compute-in-memory (CIM) architectures. However, compared to the extensively ...

However, these two non-linear operations bring significant area and power consumption for hardware implementation. To address these challenges, this paper proposes a reconfigurable floating-point ...

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