In this paper, an ALL resistive neuromorphic computing (ARNC) platform was demonstrated with Restive-gate FinFET memory, which includes three major building blocks: weight, ReLU, and ADC. The weight consists of 4-bit-per-cell RG-FinFET memory arrays with gradual and symmetrical tuning capability of the conductance, reliable endurance up to 10 5 cycles for whole 16 states, and excellent data retention. ReLU shows linear output responses when the input is positive and sharply cut-off for negative input. The ADC was implemented by a 16 parallel RG-FinFETs, featuring 267 MHz of the operation frequency, $0.28\ \mu\mathrm{W}$ of the power at V cc = 0.8V, and very small area size of 10 −5 mm 2 . It is well-suited for the energy-efficient AI-Inference in CIM.