AI Optimised Hardware
With the increasing workload in AI, there is a need to design and optimize the hardware to increase the performance and throughput of the systems to speed up training and to enable the training of more complex models. In spite of AI being for decades in the market, the development in this industry is restricted due to limited access to large data sets and improper computer architectures. The new emerging technologies like deep learning, cloud, parallel computing architectures are leading to an upgrade in AI hardware, capable of accelerating application development. By 2025, the AI hardware market is assumed to increase about 10 to 15%. The continuous growth in data availability, compute power and the developer ecosystem is demanding chip makers to build AI hardware to capture 40 to 50 percent of the total technology stack value. CPUs are combined with modern machines to solve parallel processing with dedicated hardware. GPU is a chip popularly designed to speed up multidimensional data processing such as an image. It is designed to work independently on a subspace of input data that requires heavy computing. A GPU with dedicated memory can perform repetitive functions that can be applied to various parts of the input, such as texture mapping, image rotation, translation, and filtering efficiently.