Interactive AI-powered services require low-latency evaluation of deep neural network
(DNN) models-aka"" real-time AI"". The growing demand for computationally expensive …

Recent researches on neural network have shown significant advantage in machine
learning over traditional algorithms based on handcrafted features and models. Neural …

Modern Convolutional Neural Networks (CNNs) are computation and memory intensive.
Thus it is crucial to develop hardware accelerators to achieve high performance as well as …

Field-Programmable Gate Arrays (FPGAs) have been integrated into the cloud infrastructure
to enhance its computing performance by supporting on-demand acceleration. However …

Recent research on neural networks has shown a significant advantage in machine learning
over traditional algorithms based on handcrafted features and models. Neural networks are …

A pre-trained convolutional deep neural network (CNN) is a feed-forward computation
perspective, which is widely used for the embedded systems, requires highly power-and …

Thanks to their excellent performances on typical artificial intelligence problems, deep
neural networks have drawn a lot of interest lately. However, this comes at the cost of large …

It is challenging to adopt computing-intensive and parameter-rich Convolutional Neural
Networks (CNNs) in mobile devices due to limited hardware resources and low power …

This book provides a thorough overview of the state-of-the-art field-programmable gate array
(FPGA)-based robotic computing accelerator designs and summarizes their adopted …

To address the problems of convolutional neural networks (CNNs) consuming more
hardware resources (such as DSPs and RAMs on FPGAs) and their accuracy, efficiency …