Accelerated Computing
Use of specialized hardware and software to significantly speed up computation-intensive AI tasks and processes.
Accelerated computing involves leveraging advanced hardware architectures, such as GPUs (Graphics Processing Units), TPUs (Tensor Processing Units), and FPGAs (Field-Programmable Gate Arrays), alongside optimized software frameworks and algorithms, to drastically increase the efficiency and speed of computationally demanding tasks integral to AI and ML workflows. This paradigm is significant in AI as it tackles the bottlenecks encountered by traditional CPU-bound processing, especially as AI models grow in complexity and data volumes increase. For example, deep learning tasks, which require massive parallel processing capabilities to train models on large datasets, benefit immensely from accelerated computing, reducing training times from days or weeks to mere hours. This enhancement is pivotal for real-time applications such as autonomous driving, natural language processing, and advanced robotics.
The term "accelerated computing" has been in use since the early 2000s, but it gained widespread popularity and traction within the AI community around the late 2000s and early 2010s, parallel to advancements in GPU architecture which made them a viable alternative for AI workloads.
Key contributors to the development of accelerated computing include figures and organizations like Nvidia, whose pioneering CUDA architecture enabled GPUs to be repurposed for general-purpose computing, and Google, which developed TPUs to further boost AI computations. Figures such as Jensen Huang, co-founder of Nvidia, played a crucial role in promoting and advancing the concept of accelerated computing within the AI ecosystem.