Deep learning based on neural networks has been widely used in image recognition, speech recognition, natural language processing, automatic driving, and other fields and has made breakthrough progress. FPGA stands out in the field of accelerated deep learning with its advantages such as flexible architecture and logic units, high energy efficiency ratio, strong compatibility, and low delay. In order to track the latest research results of neural network optimization technology based on FPGA in time and to keep abreast of current research hotspots and application fields, the related technologies and research contents are reviewed. This paper introduces the development history and application fields of some representative neural networks and points out the importance of studying deep learning technology, as well as the reasons and advantages of using FPGA to accelerate deep learning. Several common neural network models are introduced. Moreover, this paper reviews the current mainstream FPGA-based neural network acceleration technology, method, accelerator, and acceleration framework design and the latest research status, pointing out the current FPGA-based neural network application facing difficulties and the corresponding solutions, as well as prospecting the future research directions. We hope that this work can provide insightful research ideas for the researchers engaged in the field of neural network acceleration based on FPGA.

<abstract><p>Accurate prediction of patient-specific ventilator parameters is crucial for optimizing patient-ventilator interaction. Current approaches encounter difficulties in concurrently observing long-term, time-series dependencies and capturing complex, significant features that influence the ventilator treatment process, thereby hindering the achievement of accurate prediction of ventilator parameters. To address these challenges, we propose a novel approach called the long short-term memory relation network (LSTMRnet). Our approach uses a long, short-term memory bank to store rich information and an important feature selection step to extract relevant features related to respiratory parameters. This information is obtained from the prior knowledge of the follow up model. We also concatenate the embeddings of both information types to maintain the joint learning of spatio-temporal features. Our LSTMRnet effectively preserves both time-series and complex spatial-critical feature information, enabling an accurate prediction of ventilator parameters. We extensively validate our approach using the publicly available medical information mart for intensive care (MIMIC-III) dataset and achieve superior results, which can be potentially utilized for ventilator treatment (i.e., sleep apnea-hypopnea syndrome ventilator treatment and intensive care units ventilator treatment.</p></abstract>