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Benchmarking Machine Learning on the Edge

Graph of benchmarked inferencing time in milli-seconds for the for MobileNet v2 model and the MobileNet v1 SSD 0.75 depth model, trained using the Common Objects in Context (COCO) dataset with an input size of 300×300. Inferencing time in milli-seconds for the for MobileNet v2 model (left hand bars, blue) and the MobileNet v1 SSD 0.75 depth model (right hand bars, green), trained using the Common Objects in Context (COCO) dataset with an input size of 300×300.

Board	Framework	Connection	MobileNet v2 (ms)	MobileNet v1 (ms)
Jetson Nano	TensorFlow		309.3	276.0
Jetson Nano	TensorRT		72.3	61.6
Coral Dev Board	Edge TPU		20.9	15.7
Coral USB Accelerator	Edge TPU	USB2	58.1	49.3
Coral USB Accelerator	Edge TPU	USB3	18.2	14.9
Movidius NCS	OpenVINO	USB2	204.5	115.7
Movidius NCS	OpenVINO	USB3	176.4	88.4
Intel NCS2	OpenVIINO	USB2	118.6	87.2
Intel NCS2	OpenVINO	USB3	80.4	52.8
Raspberry Pi 3, Model B+	TensorFlow		654.0	480.3
Raspberry Pi 4	TensorFlow		483.5	263.9
Raspberry Pi 5	TensorFlow		148.9	66.2
Raspberry Pi 3, Model B+	TensorFlow Lite		379.6	271.5
Raspberry Pi 4	TensorFlow Lite		112.6	82.7
Raspberry Pi 5	TensorFlow Lite		23.5	16.9

The latest results are presented in the article benchmarking the Raspberry Pi 5.

NOTE: See the documentation directory for instructions on how to install TensorFlow and TensorFlow Lite, and how to run the benchmarking scripts on your hardware.

Getting Started with Google's Edge TPU

NOTE: These guides are likely to be out of date and are in need of updating.

Getting Started with Intel's Movidius

Getting started with the Intel Neural Compute Stick 2 and the Raspberry Pi

NOTE: These guides are likely to be out of date and are in need of updating.

Getting Started with Nvidia's GPUs

Getting started with the Nvidia Jetson Nano Developer Kit

NOTE: These guides are likely to be out of date and are in need of updating.

Benchmarking Machine Learning

TO DO

The benchmark code need to be updated to run the latest versions of the inferenecing frameworks:

benchmark_edgetpu.py - Script for Edge TPU (Coral) hardware
benchmark_intel.py - Script for Intel (Movidius) hardware using OpenVINO
benchmark_tf.py - Script for TensorFlow on generic hardware (CPU and GPU)
benchmark_tf_lite.py - Script for TensorFlow Lite on generic hardware
benchmark_tf_trt.py - Script for Nvidia Jetson hardware using TensorRT

NOTE: The benchmark_edgetpu.py script currently uses the deprecated edgetpu library, and needs to be updated to use the pycoral library. However even that library is no longer properly supported by Google.

Licence

The code in this repository is licensed under the MIT licence.

Permission is hereby granted, free of charge, to any person obtaining a copy of this software and associated documentation files (the "Software"), to deal in the Software without restriction, including without limitation the rights to use, copy, modify, merge, publish, distribute, sublicense, and/or sell copies of the Software, and to permit persons to whom the Software is furnished to do so, subject to the following conditions:

The above copyright notice and this permission notice shall be included in all copies or substantial portions of the Software.

THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE.