Deep Learning Part 3/4

Hardware is the foundation that deep learning is based on, providing its capabilities and readiness to help people categorize objects, improve speech recognition, understand visualizations, or any other purpose motivating people to use deep learning. When analyzing deep learning computational needs, remembering acronyms is the best way to spell out the hardware requirements for deep learning. GPU, TPU, FPGA, and ASICs are all key hardware components necessary for making deep learning work, especially amid concerns in recent that its progress has stunted. These types of hardware consume a lot of power and facilitate large deep learning models that CPUs and regular laptops can't manage. How does each of these hardware types facilitate these needs while addressing the computational limits restricting deep learning from achieving maximum potential?