The rapid growth of deep learning (DL) workloads has increased the need for specialized hardware accelerators that deliver high performance, energy efficiency, and scalable deploy-ment. This chapter examines the roles of graphics processing units (GPUs), field-programmable gate arrays (FPGAs), and application-specific integrated circuits (ASICs) in ac-celerating contemporary DL models across data-centre, edge, and embedded environments. Architectural characteristics, programming models, and key acceleration techniques, including dataflow optimization, quantization, sparsity exploitation, and operator fusion, are analysed to highlight performance–energy trade-offs across heterogeneous platforms. Benchmarking ap-proaches, deployment considerations, and emerging hardware–software co-design trends provided practical guidance for accelerator selection. The chapter emphasizes how hardware-accelerated deep learning reduces computational cost and energy consumption, enabling economically scalable AI deployment and supporting global economic transformation through wider AI adoption.