Heterogeneous Computing Architectures: Leveraging GPUs and FPGAs in Advanced Systems

Abstract

Heterogeneous computing architectures, which integrate different types of computing resources such as Central Processing Units (CPUs), Graphics Processing Units (GPUs), and Field-Programmable Gate Arrays (FPGAs), are becoming pivotal in modern computational systems. These architectures aim to maximize performance by assigning specific tasks to the most suitable processing unit, allowing for more efficient computation. GPUs are highly effective for parallel tasks such as graphics rendering and large-scale simulations, while FPGAs offer flexibility for customized hardware acceleration, making them ideal for real-time processing and specific algorithms. This paper explores the role of heterogeneous computing architectures in advancing computational efficiency, particularly in high-performance and embedded systems. It discusses the architectural design, the synergy between different processing units, and their applications across various domains, including artificial intelligence (AI), data processing, and scientific simulations. The paper also highlights the challenges and limitations of heterogeneous architectures, such as programming complexity, hardware integration, and power efficiency. Finally, the potential future developments of these architectures are examined, including their role in next-generation computing systems. Three tables are included to provide insights into the comparative advantages of GPUs and FPGAs, architectural components, and the challenges faced in heterogeneous systems.