In recent years, neuroscience research has revealed a great deal about the structure and operation of individual neurons, and medical tools have also revealed a great deal about how neural activity in the different regions of the brain follows a sensory stimulus. Moreover, the advances of software-based Artificial Intelligence (AI) have brought us to the edge of building brain-like functioning devices and systems overcoming the bottleneck of the conventional von Neumann computing style. The neuro-inspired technology based on spiking neural network (SNN) is one of the efficient solutions for brain-inspired cognitive computing in both learning and inference tasks. Hardware implementations of spiking neural network systems are power-efficient and effective methods to provide cognitive functions on a chip compared with the conventional stored-program computing style. Energy-efficient devises/accelerators for neural-networks are needed for power-constrained devices, such as smartphones, drones, robots, and autonomous-driving cars. We are investigating energy-efficient devices and accelerators for NNs on FPGA and ASIC. We are also investigating how to map the latest deep learning algorithms to application-specific hardware and emerging devices/systems to achieve orders of magnitude improvement in performance and energy efficiency.