High-Efficiency Neuromorphic Vlsi Architectures for Ultra-Low Power Nanoelectronic Systems

Abstract

ABSTRACT: Neuromorphic engineering is emerging as a transformative design paradigm for next-generation computing systems, especially where ultra-low power, high density, and biologically inspired adaptability are required. This paper presents a comprehensive analysis of neuromorphic VLSI architectures developed using advanced nanoelectronic devices such as memristors, carbon-nanotube FETs (CNT-FETs), and phase-change synaptic components. We examine the advantages of event-driven communication, asynchronous spike-based computation, and scalable synaptic crossbar arrays. A detailed modeling approach highlights how nano-device variability, leakage, and endurance affect the long-term reliability of neuromorphic systems. Furthermore, we propose an optimized hierarchical architecture integrating on-chip learning circuits, distributed spike-timing dependent plasticity (STDP), and thermally stable nanoscale interconnects. Performance evaluation indicates significant improvements in synaptic density, energy-per-operation, and real-time adaptive behavior. The results demonstrate that hybrid CMOS-nano neuromorphic systems can outperform traditional von Neumann architectures in sensory processing, prediction, and cognitive computing applications.

KEYWORDS: Neuromorphic VLSI, Nanoelectronics, Memristor, STDP Learning, Low-Power Architectures