Analysis of a Memcapacitor-Based Online Learning Neural Network Accelerator Framework

Abstract

Data-intensive computing tasks, such as training neural networks, are fundamental to artificial intelligence applications but often demand substantial energy resources. This study presents a novel complementary metal-oxide-semiconductor (CMOS)-based memcapacitor framework designed to address these challenges by enabling efficient and robust neuromorphic computing. Utilizing memcapacitor devices, a crossbar array that performs parallel vector-matrix multiplication operations, validated through cadence simulations and implemented in python for scalable accelerator design, is developed. The framework demonstrates outstanding performance across classification tasks, achieving 98.4% accuracy in digit recognition and 85.9% in object recognition. A key aspect of this research is its focus on real-world fabrication nonidealities, including up to 30% device parameter variations, ensuring robustness and reliability under practical deployment conditions. The results emphasize the effectiveness of capacitance-based systems in handling classification tasks while demonstrating resilience to fabrication-induced variations. This work establishes a foundation for scalable, energy-efficient, and robust memcapacitor-based neural networks, advancing the potential for intelligent systems in artificial intelligence-driven applications and paving the way for future innovations in neuromorphic computing.