Convolutional neural networks (CNNs) are classical models for computer vision and machine learning applications such as video surveillance, pattern recognition, weather forecasting, traffic, and safety. CNNs involve computationally intensive operations and require huge off-chip memory bandwidth, which makes it a challenging task to deploy on real-time embedded systems. Compared to central processing units and graphic processing units, field programmable gate arrays (FPGA)-based CNNs are gaining popularity owing to their flexibility and efficiency. In this work, we present an efficient CNN accelerator based on blocked Winograd-GEMM architecture with high performance. We implement ResNet-18 CNN model on XC7VX690T FPGA using proposed architecture. This implementation operates at a clock frequency of 200 MHz and gives average throughput of 383 GOPS which is comparable to other state-of-art implementations. This manuscript is an extended version of [S. Kala, J. Mathew, B. R. Jose, and S. Nalesh, “UniWiG: Unified Winograd-GEMM Architecture for Accelerating CNN on FPGAs,” in 2019 32nd International Conference on VLSI Design and 2019 18th International Conference on Embedded Systems (VLSID), Delhi, NCR, India, 2019, pp. 209–214. DOI: 10.1109/VLSID.2019.00055.].

卷积神经网络（CNN）是用于计算机视觉和机器学习应用程序（例如视频监视，模式识别，天气预报，交通和安全性）的经典模型。CNN涉及计算密集型操作，并且需要巨大的片外内存带宽，这使得在实时嵌入式系统上进行部署成为一项艰巨的任务。与中央处理单元和图形处理单元相比，基于现场可编程门阵列（FPGA）的CNN由于其灵活性和效率而越来越受欢迎。在这项工作中，我们提出了一种基于高性能的基于阻塞Winograd-GEMM架构的高效CNN加速器。我们使用提出的架构在XC7VX690T FPGA上实现ResNet-18 CNN模型。该实施方案以200 MHz的时钟频率运行，平均吞吐量为383 GOPS，可与其他最新的实施方案相媲美。该手稿是[S. Kala，J。Mathew，BR Jose和S. Nalesh，“ UniWiG：用于在FPGA上加速CNN的统一Winograd-GEMM架构”，2019年第32届VLSI设计国际会议和2019年第18届嵌入式系统国际会议（VLSID），印度，NCR，德里，2019年，第209-214页。DOI：10.1109 / VLSID.2019.00055。]。