Neuromorphic Chips: Scalable Memristor Crossbar Architectures for Energy-Efficient Edge-AI

Abstract - Neuromorphic computing has emerged as a revolutionary area in contemporary information processing by emulating the architecture and operation of the human brain to enable artificial intelligence. In contrast with traditional von Neumann systems like CPUs and GPUs, which are plagued by excessive energy use, latency, and limited scalability because of the divide between memory and computation, neuromorphic chips integrate these two functions into a single platform. At the center of this architecture are memristor crossbar arrays, which support in-memory computation, parallel processing, and adaptive learning, thus eliminating the von Neumann bottleneck. Such integration is especially critical for Edge-AI applications, where devices are required to provide real-time intelligence, work with tight energy budgets, and work without relying on cloud resources. Based on the seminar theme, this review focuses on scalable neuromorphic chip architectures and their uses in applications ranging from robotics, healthcare, IoT, and smart cities. Comparative evaluation with traditional processors features enhanced energy efficiency, computational throughput, and on-chip adaptability. However, issues like memristor variability, device endurance, and fabrication complexity are still open issues for large-scale deployment. Neuromorphic chips stand at the intersection of potential future research avenues, ranging from hybrid CMOS–memristor integration to three-dimensional crossbar structures and algorithm–hardware co-design, as a central enabler of next-generation, energy-efficient, intelligent edge computing systems. Key Words: neuromorphic chip, memristor crossbar, Edge-AI, in-memory computing, spiking neural networks, low power.