Hardware Accelerators for Artificial Intelligence

TL;DR

This paper provides a comprehensive overview of specialized hardware accelerators like GPUs, FPGAs, and ASICs, highlighting their roles, challenges, and future prospects in enhancing AI applications and workloads.

Contribution

It offers an in-depth analysis of AI-specific hardware accelerators, covering their development, types, challenges, and future directions in AI hardware evolution.

Findings

GPU, FPGA, and ASIC accelerators improve AI workload efficiency

Design challenges include power, scalability, and integration issues

Future AI hardware will focus on specialization and energy efficiency

Abstract

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.