FPGA acceleration of convolutional neural networks: Current applications and future prospects

Abstract

In the contemporary era of big data, the utilization of deep learning technology has become widespread in the extraction of valuable analytical insights from the data under consideration. This technology finds extensive applications in various domains, including image information recognition, speech processing, and text language analysis. When it comes to accelerating convolutional neural networks (CNNs), the utilization of Field-Programmable Gate Arrays (FPGAs) offers distinct advantages over other hardware accelerators. Nevertheless, it is important to acknowledge that FPGA-based acceleration also comes with its own structural limitations. This article places its focus on two main aspects: firstly, it delves into the present-day application landscape and the latest developmental trajectories of convolutional neural networks. Secondly, it elucidates the inherent characteristics of FPGA implementations of CNNs. Furthermore, the article conducts a comprehensive examination of the pertinent constraints associated with FPGA-accelerated deep learning algorithms. The discussion extends beyond the present moment and ventures into the future, offering insights into the potential advancements in the field of deep learning. Importantly, it also brings into focus the prospective avenues for further research regarding the application of FPGAs in the domain of convolutional neural networks.