What technology advancement has spurred the growth in the use of deep neural networks?

The growth in the use of deep neural networks has been significantly spurred by advancements in GPU hardware technology. Graphics Processing Units (GPUs) are designed to handle a large number of parallel operations, which makes them particularly suited for the complex calculations required in deep learning models. Unlike traditional CPUs, which excel in sequential processing, GPUs can perform thousands of calculations simultaneously, accelerating the training of deep neural networks.

This parallel processing capability allows researchers and practitioners to train larger models on vast datasets in a fraction of the time it would take using standard CPUs. As a result, the accessibility and efficiency of utilizing deep neural networks have greatly improved, leading to widespread adoption across various applications such as image and speech recognition, natural language processing, and more.

While cloud computing has facilitated access to computing resources and allowed for scalable training, it is the underlying GPU technology that truly empowers the rapid development and deployment of deep learning models. Quantum computing is still largely experimental and not yet widely applied in this context, and traditional CPU technology lacks the parallel processing power required for efficient deep learning tasks.