CNN implementation in Resource Limited FPGAs - Key Concepts and Techniques

Convolutional Neural Network (CNN) is a type of algorithm used to solve complex problems with a superior performance when compared to traditional computational methods. Currently, there is a tendency to migrate CNN implementations from the cloud to the edge (closer to the data source) in order to reduce both latency and communication bandwidth and at the same time, increase security and system efficiency. Field Programmable Gate Array (FPGA) is a good option for implementing CNN in the edge, since even the lowest cost FPGAs have a good energy efficiency and a sufficient throughput to enable real-time applications. In this paper, key concepts about CNN are reviewed. Next, the most popular compression methods used in the CNN training phase are described. Finally, we present the most popular frameworks for hardware accelerator design and key hardware optimization techniques used by many of those frameworks to enable CNN inference on resource limited devices