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Hardware Accelerators for Artificial Intelligence

S M Mojahidul Ahsan, Anurag Dhungel, Mrittika Chowdhury, Md Sakib Hasan, Tamzidul Hoque
University of Kansas
arXiv:2411.13717 [cs.AR], (20 Nov 2024)

@misc{ahsan2024hardwareacceleratorsartificialintelligence,

   title={Hardware Accelerators for Artificial Intelligence},

   author={S M Mojahidul Ahsan and Anurag Dhungel and Mrittika Chowdhury and Md Sakib Hasan and Tamzidul Hoque},

   year={2024},

   eprint={2411.13717},

   archivePrefix={arXiv},

   primaryClass={cs.AR},

   url={https://arxiv.org/abs/2411.13717}

}

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In this chapter, we aim to explore an in-depth exploration of the specialized hardware accelerators designed to enhance Artificial Intelligence (AI) applications, focusing on their necessity, development, and impact on the field of AI. It covers the transition from traditional computing systems to advanced AI-specific hardware, addressing the growing demands of AI algorithms and the inefficiencies of conventional architectures. The discussion extends to various types of accelerators, including GPUs, FPGAs, and ASICs, and their roles in optimizing AI workloads. Additionally, it touches on the challenges and considerations in designing and implementing these accelerators, along with future prospects in the evolution of AI hardware. This comprehensive overview aims to equip readers with a clear understanding of the current landscape and future directions in AI hardware development, making it accessible to both experts and newcomers to the field.
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