Review on Modern Mathematics in the Analysis of Artificial Intelligence Data and Innovative Devices

Abstract

The current review is concerned with the principle of modern mathematics in the analysis of artificial intelligence data and innovative devices, although the performance of both graphics processing units (GPUs) and programmable logic gates (FPGAs) greatly outperforms that of CPUs, a factor of 10 in Efficiency may be achieved using a more precise design using an application-specific integrated circuit (ASIC). These accelerators employ strategies such as improved memory usage and low precision arithmetic to speed up computational operations and computing throughput. Some low-resolution floating-point formats use AI acceleration and become half-accurate and bfloat16. Deep learning frameworks are still in the midst of development which has made it difficult to design custom computer hardware, while reconfigurable devices such as Programmable Logic Gate Arrays (FPGAs) have made it easier to develop hardware, frameworks, and software together. Microsoft used FPGAs to speed inference and its application to AI prompted Intel to acquire Altera to integrate FPGAs into the server. CPUs that would be able to accelerate AI as well as general-purpose tasks.