Artificial Intelligence (AI) Accelerator

Abstract

In the computer idea and many prevalent machine learning tasks, such as language recognition and fraud face detection, there has been immense importance of Convolutional neural networks (CNNs). Artificial intelligence accelerator is used to run many Machine Learning algorithms. Various hardware platforms are used to support its processing. However, there are many challenges that need to be addressed for the successful computation of these algorithms such as high computational processing, cost efficiency and low power consumption at the same time. Field Programmable Gate Array (FPGA) technology can be customized to meet the specific requirements for the implementation of ML algorithms. The use of FPGA in deep learning has been increasingly significant due its capacity for maximizing parallelism and energy efficiency. Convolutional Neural Networks (CNNs) have become the benchmark in bringing high accuracy in many applications using machine learning or deep learning and speech recognition. For faster and speedy results, we need to accelerate CNN algorithms. FPGA has exceptional features which make it an achiever in accelerating deep learning algorithm. The prominent features are flexibility, low latency, and high-power efficiency. Flexibility allows us to customize hardware even down to its bit level. It becomes a competitive in its feature when precision and accuracy is needed in deep learning algorithm.