Accelerator of Stacked Convolutional Independent Subspace Analysis for Deep Learning-Based Action Recognition
Lü He, Yan Luo, Yu Cao · 2014
Action recognition has been a research challenge in multimedia computing and machine vision. Recent advances in deep learning combined with stacked convolutional Independent Subspace Analysis (ISA) has achieved a better performance superior to all previously published results on several public available data sets. Unfortunately, one major issue in large-scale deployment of this new deep learning-based approach is the unacceptable latency of training with high-dimension data. In this paper, we propose a new hardware accelerator that can reduce the training time substantially for deep learning-based action recognition. Specifically, our proposed approach focuses on accelerating the convolutional stacked ISA algorithm, the core components of the deep learning-based action recognition algorithms. We design parallel pipelines, data parallelisms and look-up table to speed up the algorithm. With an embedded heterogeneous platform consisting of a general purpose processor and a FPGA, we are able to achieve up to 10X speedup for stacked ISA training compared to a software-only implementation.