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Abstract
The integration of reinforcement learning (RL) with neural architectures in Field Programmable Gate Arrays (FPGA) represents a transformative approach for accelerating semiconductor innovation and enabling high-performance Very-Large-Scale Integration (VLSI) applications. This research explores the synergistic interplay between RL algorithms and FPGA frameworks to optimize hardware efficiency, reduce latency, and improve power consumption in advanced semiconductor systems. Specifically, the study highlights the application of neural architectures such as Convolutional Neural Networks (CNNs) and deep learning models within FPGA environments, focusing on VLSI signal processing, adaptive workloads, and cost-sensitive designs. A comprehensive analysis of recent literature reveals significant advancements while identifying critical challenges in scalability and dynamic adaptation. Through detailed evaluations and performance benchmarks, this paper emphasizes the potential of RL-augmented FPGA designs to redefine paradigms in high-performance computing.
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