Benchmarking of CNNs for Low-Cost, Low-Power Robotics Applications
Available benchmarks for object classification and detection using Convolutional Neural Networks (CNNs) focus on evaluating accuracy only. This is reflected in the state-of-the-art
Three embedded platforms are used for performing inference: Movidius NCS, Raspberry Pi 3, Intel Joule The power consumption is measured by sampling on the power lines using the INA219 power monitor setup at a sampling rate of ~500Hz. For the Raspberry Pi and Joule, Caffe and TensorFlow frameworks are used to perform inference.
|Network in Network||16||~2 million|
The obtained inference times and power consumption show that by using the NCS some networks are up to 4x faster than running on other embedded platforms while keeping a low-power consumption. This is critical for battery powered low-cost robots. Future work includes the addition of more networks for the benchmark on more platforms and the creation of a public database for the obtained results.