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The article presents a floating-point SRAM computing-in-memory (FP-CIM) macro designed for convolutional neural networks (CNNs) to enhance energy efficiency and computational precision. Key innovations include a dynamic maximum exponent configuration, a dual-bit wordline-separated SRAM cell, and customized adder designs, which collectively improve performance while maintaining high inference accuracy. Simulation results indicate that the macro achieves an energy efficiency of 4.81 TFLOPS/W and an accuracy o

Author

mohit710300

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EN