Extracting per-frame features using convolutional neural networks for
real-time processing of video data is currently mainly performed on powerful
GPU-accelerated workstations and compute clusters. However, there are many
applications such as smart surveillance cameras that require or would benefit
from on-site processing. To this end, we propose and evaluate a novel algorithm
for change-based evaluation of CNNs for video data recorded with a static
camera setting, exploiting the spatio-temporal sparsity of pixel changes. We
achieve an average speed-up of 8.6x over a cuDNN baseline on a realistic
benchmark with a negligible accuracy loss of less than 0.1% and no retraining
of the network. The resulting energy efficiency is 10x higher than that of
per-frame evaluation and reaches an equivalent of 328 GOp/s/W on the Tegra X1
platform.

本论文提出一种新的算法，利用像素变化的时空稀疏性，对静态摄像机录制的视频数据进行卷积神经网络的基于变化的评估，从而实现本地视频数据的处理，其速度比 cuDNN 基准测试平均快 8.6 倍，精度损失不到 0.1％，不需要对网络进行重新训练，而能源效率可达 328 GOp /s/ W。