Traditional computing architectures based on the von Neumann model face inefficiencies when processing massively parallel and event-driven Artificial Intelligence (AI) workloads, suffering from memory–computation bottlenecks and high power consumption. Neuromorphic computing, inspired by biological neural systems, addresses these challenges through asynchronous, event-driven processing with low latency and high energy efficiency. Recent advances in neuromorphic hardware have further promoted algorithm–hardware co-design to improve adaptability and scalability in real-time and edge computing. However, these benefits are often constrained by the lack of accessible development tools, standardized methodologies, and comprehensive documentation. Existing implementations frequently derive from research prototypes tailored to specific experiments rather than reusable, structured libraries, making new developments complex and often dependent on expert intervention.