Design and Implementation of Nanoelectronics-Based Advanced Associative Memory Architecture for Autonomous Vehicles

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The increasing demand for high-speed and energy-efficient memory solutions in autonomous vehicles has led to the development of advanced memory architectures. This paper presents the design and implementation of a Nanoelectronics-Based Advanced Associative Memory Architecture (NAAMA) as an alternative to Ternary Content Addressable Memory (TCAM) for real-time decision-making in autonomous vehicles. The proposed memory system enhances pattern matching efficiency while reducing power consumption and latency.The architecture leverages nanoelectronic devices, including memristors and FinFET-based memory cells, to improve performance and scalability. A hybrid SRAM and memristor-based associative memory design is implemented to optimize storage efficiency and search operations. The proposed model is evaluated in terms of power consumption, search speed, area efficiency, and scalability using Cadence Virtuoso and HSPICE simulations at a 7 nm technology node. Experimental results show that the proposed NAAMA achieves a 36% reduction in power consumption, a 28% improvement in search latency, and a 42% increase in area efficiency compared to conventional TCAM implementations. The integration of nanoelectronic components significantly enhances the system’s ability to perform high-speed parallel searches, making it a viable solution for real-time applications in autonomous vehicle decision-making systems. The study also demonstrates the robustness of the proposed architecture under varying traffic and environmental conditions, ensuring reliable and accurate decision-making for autonomous navigation. The findings suggest that nanoelectronics-based associative memory architectures can offer substantial advantages in energy efficiency, computational speed, and integration density, paving the way for future innovations in autonomous vehicle computing and intelligent transportation systems.