FlexCNN: An End-to-end Framework for Composing CNN Accelerators on FPGA

Abstract

With reduced data reuse and parallelism, recent convolutional neural networks (CNNs) create new challenges for FPGA acceleration. Systolic arrays (SAs) are efficient, scalable architectures for convolutional layers, but without proper optimizations, their efficiency drops dramatically for reasons: (1) the different dimensions within same-type layers, (2) the different convolution layers especially transposed and dilated convolutions, and (3) CNN’s complex dataflow graph. Furthermore, significant overheads arise when integrating FPGAs into machine learning frameworks. Therefore, we present a flexible, composable architecture called FlexCNN, which delivers high computation efficiency by employing dynamic tiling, layer fusion, and data layout optimizations. Additionally, we implement a novel versatile SA to process normal, transposed, and dilated convolutions efficiently. FlexCNN also uses a fully pipelined software-hardware integration that alleviates the software overheads. Moreover, with an automated compilation flow, FlexCNN takes a CNN in the ONNX1 representation, performs a design space exploration, and generates an FPGA accelerator. The framework is tested using three complex CNNs: OpenPose, U-Net, and E-Net. The architecture optimizations achieve 2.3× performance improvement. Compared to a standard SA, the versatile SA achieves close-to-ideal speedups, with up to 5.98× and 13.42× for transposed and dilated convolutions, with a 6% average area overhead. The pipelined integration leads to a 5× speedup for OpenPose.