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CBinfer: Change-Based Inference for Convolutional Neural Networks on Video Data

Abstract · Apr 14, 2017 00:36 ·

inference cnns tegra zurich frame surveillance video embedded eth cs-cv cs-lg

Arxiv Abstract

  • Lukas Cavigelli
  • Philippe Degen
  • Luca Benini

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 per-frame evaluation and reaches an equivalent of 328 GOp/s/W on the Tegra X1 platform.

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