Broadly speaking, the goal of neuromorphic engineering is to build computer systems that mimic the brain. Spiking Neural Network(SNN) is a type of biologically-inspired neural networks that perform information processing based on discrete-time spikes, different from traditional Artificial Neural Network(ANN).Hardware implementation of SNNs is necessary for achieving high-performance and low-power. We present the Darwin Neural Processing Unit(NPU), a neuromorphic hardware co-processor based on SNN implemented with digitallogic, supporting a maximum of 2048 neurons, 20482= 4194304 synapses, and 15 possible synaptic delays.The Darwin NPU was fabricated by standard 180 nm CMOS technology with an area size of 5 × 5 mm2and70 MHz clock frequency at the worst case. It consumes 0.84 m W/MHz with 1.8 V power supply for typical applications. Two prototype applications are used to demonstrate the performance and efficiency of the hardware implementation. Broadly speaking, the goal of neuromorphic engineering is to build computer systems that mimic the brain. Spiking Neural Network (SNN) is a type of biologically-inspired neural networks that perform information processing based on discrete-time spikes, different from traditional Artificial Neural Network (ANN) .Hardware implementation of SNNs is necessary for achieving high-performance and low-power. We present the Darwin Neural Processing Unit (NPU), a neuromorphic hardware co-processor based on SNN implemented with digitallogic, supporting a maximum of 2048 neurons , 20482 = 4194304 synapses, and 15 possible synaptic delays. The Darwin NPU was fabricated by standard 180 nm CMOS technology with an area size of 5 × 5 mm2 and 70 MHz clock frequency at the worst case. It consumes 0.84 mW / MHz with 1.8 V power supply for typical applications. Two prototype applications are used to demonstrate the performance and efficiency of the hardware implementation.