The human brain is renowned for its vast parallel reconfigurable synapses that connect billions of neurons, playing a crucial role in learning and adaptability. The synaptic weight represents the strength of the connection between two neurons. Spiking Neural Networks (SNNs) leverage this biological inspiration for applications ranging from vision systems to brain-computer interfaces. Traditionally, the design of these systems has focused on fixed functionality using off-the-shelf components, which lack the flexibility to adapt to various computing environments. In contrast, the reconfigurable design approach supports multiple target applications through dynamic reconfigurability, network topology independence, and expandability. This chapter explores the architecture and hardware design of a reconfigurable neuromorphic processor. The architecture features an SNN that can be reconfigured to recover from faults using suitable methods that employ Field-Programmable Gate Arrays (FPGAs) without relying on proprietary intellectual property. This reconfigurable approach facilitates the implementation of neuromorphic processors in Application-Specific Integrated Circuits (ASICs), enhancing their versatility and robustness.