Neuromorphic Computing Architectures for EnergyEfficient Edge Devices in Autonomous Vehicles

Autonomous vehicles hold the promise of significantly reducing road accidents caused by human error, such as distracted driving or impaired judgment. However, the widespread adoption of autonomous vehicles faces challenges, including regulatory frameworks, liability concerns, ethical considerations, and the need for comprehensive infrastructure updates. This research focuses on the development of neuromorphic computing architectures tailored for energyefficient edge devices in autonomous vehicles. The significance of this study lies in addressing the escalating computational demands of real-time data processing in autonomous driving scenarios while mitigating the inherent power constraints of edge computing environments. The conventional approaches in autonomous vehicle computing, often centralized and powerintensive, pose challenges in terms of energy consumption and real-time responsiveness. The proposed neuromorphic computing architectures leverage spiking neural networks and event-driven processing to emulate the brain's efficiency, offering a novel solution to enhance both computational performance and energy efficiency. The research addresses prior issues related to the power-hungry nature of traditional computing paradigms in autonomous vehicles and emphasizes the need for more sustainable and responsive edge computing solutions. The novelty of this research lies in the integration of neuromorphic principles into edge devices, enabling onboard processing that is not only energy-efficient but also well-suited for the sporadic and dynamic nature of real-world driving environments. This study paves the way for advancements in autonomous vehicle technology, promising enhanced energy efficiency, reduced latency, and improved overall system performance.