CBinfer: Exploiting Frame-to-Frame Locality for Faster Convolutional Network Inference on Video Streams
This enables faster and more energy-efficient convolutional network inference on video streams for embedded platforms, addressing a bottleneck in real-time applications like surveillance.
The paper tackled the problem of high computational effort for real-time video inference on embedded platforms by exploiting spatio-temporal sparsity of pixel changes, achieving average speed-ups of 9.1x for semantic segmentation and 7.0x for pose detection with negligible accuracy loss (<0.1%).
The last few years have brought advances in computer vision at an amazing pace, grounded on new findings in deep neural network construction and training as well as the availability of large labeled datasets. Applying these networks to images demands a high computational effort and pushes the use of state-of-the-art networks on real-time video data out of reach of embedded platforms. Many recent works focus on reducing network complexity for real-time inference on embedded computing platforms. We adopt an orthogonal viewpoint and propose a novel algorithm exploiting the spatio-temporal sparsity of pixel changes. This optimized inference procedure resulted in an average speed-up of 9.1x over cuDNN on the Tegra X2 platform at a negligible accuracy loss of <0.1% and no retraining of the network for a semantic segmentation application. Similarly, an average speed-up of 7.0x has been achieved for a pose detection DNN and a reduction of 5x of the number of arithmetic operations to be performed for object detection on static camera video surveillance data. These throughput gains combined with a lower power consumption result in an energy efficiency of 511 GOp/s/W compared to 70 GOp/s/W for the baseline.