Accelerating convolutional neural networks: Exploring FPGA-based architectures and challenges

Abstract

This article provides an in-depth exploration of the principles of Convolutional Neural Network (CNN) and Field Programmable Gate Array (FPGA), focusing on the reasons that make FPGAs well-suited for accelerating CNN algorithms. The discussion begins with an overview of CNN and FPGA fundamentals, highlighting the potential advantages of utilizing FPGAs in accelerating CNN computations. Next, the article introduces four distinct FPGA-based CNN accelerator designs, each presenting its unique creative architecture. These designs showcase a range of characteristics, including reconfigurability, parameterizable, and energy efficiency. The article delves into a detailed analysis of each design, elucidating their innovative aspects and potential benefits in CNN acceleration. By thoroughly understanding the proposed design approaches, the author emphasizes the challenges that arise when implementing FPGA-based CNN accelerators. These challenges encompass aspects such as algorithm mapping, hardware resource utilization, and achieving a balance between flexibility and efficiency. Additionally, the article sheds light on the future prospects of FPGA-based CNN accelerators, exploring potential advancements and research directions in this domain.