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This paper presents an energy-efficient all-digital time-domain compute-in-memory (TDCIM) macro optimized for binary neural networks (BNNs), which enhances computing efficiency and accuracy through techniques like wave-pipelining and shared computing chains. Implemented in 22nm FDSOI technology, the TDCIM macro achieves an energy efficiency of 1.2 POPS/W while maintaining high accuracy on the MNIST dataset. The design addresses challenges of deploying neural networks on edge devices by minimizing data movem

Author

mohammadreza.hajipour98

Language

EN