Analytics Vidhya
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Analytics Vidhya

Optimizing arch64 Edge devices for Maximum Performance on ML

  1. Jetson Nano
  2. Jetson TX1
  3. Jetson TX2
  4. Jetson AGX Xavier
  1. Run the Jetson clock.
$ sudo
$ fallocate -l 8G swapfile
$ sudo chmod 600 swapfile
$ sudo mkswap swapfile
$ sudo swapon swapfile
$ free -m
  1. Max Q
fig. Energy profiles for arch64[wikipedia]
sudo nvpmodel -m <mode number for desired profile>
  • Convolution Core — optimized high-performance convolution engine.
  • Single Data Processor — single-point lookup engine for activation functions.
  • Planar Data Processor — planar averaging engine for pooling.
  • Channel Data Processor — multi-channel averaging engine for advanced normalization functions.
  • Dedicated Memory and Data Reshape Engines — memory-to-memory transformation acceleration for tensor reshape and copy operations.
  • Compilation of deep learning models in Keras, MXNet, PyTorch, Tensorflow, CoreML, DarkNet into minimum deployable modules on diverse hardware backends.
  • Infrastructure to automatic generate and optimize tensor operators on more backend with better performance.



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