Skeleton-based Graph Convolutional Networks (GCNs) models for action recognition have achieved excellent prediction accuracy in the field. However, limited by large model and computation complexity, GCNs for action recognition like 2s-AGCN have insufficient power-efficiency and throughput on GPU. Thus, the demand of model reduction and hardware acceleration for low-power GCNs action recognition application becomes continuously higher. To address challenges above, this paper proposes a runtime sparse feature compress accelerator with hybrid pruning method: RFC-HyPGCN. First, this method skips both graph and spatial convolution workloads by reorganizing the multiplication order. Following spatial convolution workloads channel-pruning dataflow, a coarse-grained pruning method on temporal filters is designed, together with sampling-like fine-grained pruning on time dimension. Later, we come up with an architecture where all convolutional layers are mapped on chip to pursue high throughput. To further reduce storage resource utilization, online sparse feature compress format is put forward. Features are divided and encoded into several banks according to presented format, then bank storage is split into depth-variable mini-banks. Furthermore, this work applies quantization, input-skipping and intra-PE dynamic data scheduling to accelerate the model. In experiments, proposed pruning method is conducted on 2s-AGCN, acquiring 3.0x-8.4x model compression ratio and 73.20\% graph-skipping efficiency with balancing weight pruning. Implemented on Xilinx XCKU-115 FPGA, the proposed architecture has the peak performance of 1142 GOP/s and achieves up to 9.19x and 3.91x speedup over high-end GPU NVIDIA 2080Ti and NVIDIA V100, respectively. Compared with latest accelerator for action recognition GCNs models, our design reaches 22.9x speedup and 28.93\% improvement on DSP efficiency.

翻译：以Skeleton为基础的基于 Scleton 的图表变速网络(GCNs) 行动识别模型(GCNs) 已经在实地实现了极好的预测准确性。 但是,由于大模型和计算复杂性的限制, 2s-AGCN 等用于行动的GCN 行动识别GPU没有足够的动力效率和吞吐量。 因此,对低功率 GCN 动作识别应用程序的模型减少和硬件加速需求不断提高。 为了应对上述挑战,本文件建议建立一个运行时间稀少的功能压缩缩压加速器,配有混合处理法: RFC-HyPGCNCN。 首先,这一方法通过重组倍增量顺序, 图形和空间变速重的重量都跳过图表和空间变速。 在空间变速工作量变速速度中, 3. OP- 8SDFS 运行数据流动数据流流流流流流流流量数据流流流流流量数据流流量数据流流流流后, 预示着22个变速的GPEPEO 格式。