Abstract

In System-on-Chip (SoC) architectures, the integration of on-chip neural networks has emerged as a promising avenue for augmenting computational capabilities. This research addresses the imperative need to seamlessly embed AI-driven neural networks directly into SoC designs, paving the way for efficient, real-time processing of complex tasks. Current SoC architectures often grapple with limitations in handling intricate computations and real-time decision-making, prompting the exploration of innovative solutions. The research identifies a critical research gap in the seamless integration of on-chip neural networks, which hinders the realization of optimal performance gains. Bridging this gap requires a comprehensive methodology that encompasses the design, implementation, and optimization of on-chip neural networks within the SoC framework. The study leverages advanced machine learning algorithms and hardware-accelerated techniques to enhance the efficiency and speed of on-chip neural network operations. The methodology involves a multi-faceted approach, incorporating algorithmic refinement, hardware optimization, and parallel processing strategies. The research meticulously evaluates the impact of on-chip neural networks on SoC performance metrics, including power consumption, latency, and throughput. Experimental results demonstrate the feasibility and advantages of the proposed integration, showcasing significant improvements in computational efficiency and real-time processing capabilities.