Specialized Hardware Accelerators for AI on Mobile Platforms

Abstract

Abstract: The growing demand for AI capabilities in mobile platforms necessitates the development of specialized hardware accelerators designed to optimize performance, energy efficiency, and real-time processing. As mobile devices become more capable, AI-driven applications, such as augmented reality, autonomous systems, and personalized services, require hardware that can handle complex computations without compromising battery life or device size. Specialized hardware accelerators, such as Application-Specific Integrated Circuits (ASICs), Field Programmable Gate Arrays (FPGAs), and Graphics Processing Units (GPUs), have emerged as solutions to meet the increasing computational demands of AI applications on mobile platforms. This paper explores the role of these accelerators in enhancing the performance of AI workloads, comparing their capabilities in terms of processing power, energy consumption, and integration into mobile devices. The integration of such accelerators into mobile systems provides significant improvements in speed and power efficiency, enabling on-device AI computations and reducing reliance on cloud processing. Furthermore, the paper discusses the challenges involved in designing these accelerators, such as thermal management, compatibility with mobile architectures, and balancing the trade-off between performance and energy consumption. In addition, future trends such as the application of machine learning models in accelerator design and the potential impact of quantum computing on mobile AI are considered. This work contributes to understanding the importance of specialized hardware in the evolution of AI-enabled mobile devices and its role in supporting next-generation applications.