LAUSR

Neuromorphic computing is expected to realize fast and energy-efficient artificial neural networks and address the inherent limitations of von Neumann architectures in dedicated communication applications. To realize this vision, we identify the existing challenges in neuromorphic computing and provide a specific solution from the perspectives of device, circuit, and system. At the device level, we fabricate a metal-oxide-based memristor with high stability, low power, and good scalability, serving as the fundamental component of a neuromorphic computing system. At the circuit level, the basic circuit units and necessary peripheral circuits are designed to realize efficient vector-matrix multiplication and different functions, including nonlinear activation operation, subtraction operation, added operation, and so on. At the system level, a flexible neuromorphic computing system with a hardware-friendly training approach is proposed, which can perform effective communication with good trade-off between accuracy and time consumption. This study is expected to achieve the deep integration of nanotechnology, energy-efficient integrated circuits, and neuromorphic computing systems into communication applications.