Model Parallelism Optimization for CNN FPGA Accelerator

Convolutional neural networks (CNNs) have made impressive achievements in image classification and object detection. For hardware with limited resources, it is not easy to achieve CNN inference with a large number of parameters without external storage. Model parallelism is an effective way to reduce resource usage by distributing CNN inference among several devices. However, parallelizing a CNN model is not easy, because CNN models have an essentially tightly-coupled structure. In this work, we propose a novel model parallelism method to decouple the CNN structure with group convolution and a new channel shuffle procedure. Our method could eliminate inter-device synchronization while reducing the memory footprint of each device. Using the proposed model parallelism method, we designed a parallel FPGA accelerator for the classic CNN model ShuffleNet. This accelerator was further optimized with features such as aggregate read and kernel vectorization to fully exploit the hardware-level parallelism of the FPGA. We conducted experiments with ShuffleNet on two FPGA boards, each of which had an Intel Arria 10 GX1150 and 16GB DDR3 memory. The experimental results showed that when using two devices, ShuffleNet achieved a 1.42x speed increase and reduced its memory footprint by 34%, as compared to its non-parallel counterpart, while maintaining accuracy.

Automated summary

In this work, a novel model parallelism method is proposed to decouple the CNN structure, reducing memory footprint and eliminating inter-device synchronization. A parallel FPGA accelerator for ShuffleNet is designed using this method, which achieves a 1.42x speed increase and reduces memory footprint by 34%, while maintaining accuracy.