Enhancing Digital Signal Processing: Obstacles and Advancements in FPGAand ASIC Hardware Implementations

Abstract

This review study explores progress in digital signal processing (DSP) utilizing fieldprogrammable gate arrays (FPGAs) and application-specific integrated circuits (ASICs). FPGAs provide adaptability and reconfigurability, rendering them appropriate for situations necessitating frequent enhancements. Engineers design ASICs to achieve maximum performance and efficiency in specific, consistent applications. The study addresses difficulties like optimizing computational efficiency, minimizing energy usage, improving algorithmic implementation, and overcoming resource limitations. Recent advancements in DSP hardware include AI integration, chip miniaturization, and enhanced synthesis tools. These technologies are revolutionizing DSP technology, improving machine learning applications, reducing power consumption, managing algorithmic complexity, optimizing resource allocation, and assuring scalability within hardware constraints. The study examines advancements in secure DSP hardware, sustainable chip technology, and the potential for hybrid quantum computing applications. The study underscores the necessity for ongoing research and innovation to tackle contemporary difficulties and improve the capabilities of DSP hardware across several industries, including telecommunications, healthcare, and automobiles.