Google Coral Edge TPU Board Vs NVIDIA Jetson Nano Dev board — Hardware Comparison

Both NVidia and Google recently released dev board targeted towards EdgeAI and also at a cost point to attract developers, makers and hobbyists. Both the dev boards are primarily for inference, but support limited transfer learning re-training. The Edge TPU supports transfer learning training using weight imprinting technique. Both of the dev kits consists of a SOM (System-on-Module) connected to a dev board which has various connectors like USB, Ethernet, microSD slots etc. This is a comparison of the hardware for the two dev kits which can be used as Single board computer (SBC) and not the Edge TPU USB stick. If you don’t want to read the whole article, in my opinion the Coral Edge dev kit is slightly better value for the money as it includes essential peripherals like Wifi and Bluetooth however the Jetson Nano has better software support (both INT8 and FP16 Inference).

Coral Edge TPU Dev board

The size of the whole kit is — 88 mm x 60 mm x 22 mm while the size of the SOM only is — 48 mm x 40 mm x 5 mm. So people can also design their own base boards of different form factor and connect to the SOM. The board only comes with a u-boot bootloader and later one can load a image like Mendel linux.

The NXP iMXM processor on the Coral SOM also has a Vivante GC7000 lite graphics GPU — could it be used anything other than graphics? Detailed specs — https://coral.withgoogle.com/docs/dev-board/datasheet/

Buy it here — https://coral.withgoogle.com/products/dev-board

Image from — https://coral.withgoogle.com/products/dev-board/

NVIDIA Jetson Nano Dev kit

Like the coral board here also a SOM connects to the baseboard. The Jetson SOM is slightly bigger — 69.6 mm x 45 mm. The board comes with Ubuntu 18.04 based environment.

Detailed specs — https://www.nvidia.com/en-us/autonomous-machines/embedded-systems/jetson-nano/

Buy it here — https://www.nvidia.com/en-us/autonomous-machines/embedded-systems/jetson-nano/

NVidia Jetson Dev kit — https://www.nvidia.com/en-us/autonomous-machines/embedded-systems/jetson-nano/
Jetson SOM — https://blog.hackster.io/introducing-the-nvidia-jetson-nano-aaa9738ef3ff

Below is a comparison of the hardware features of the two boards

Performance

Nvidia has provided some performance comparison for Jetson Nano with other SBC like Raspberry Pi 3 , Google Coral Edge TPU board — https://devblogs.nvidia.com/jetson-nano-ai-computing/

Data from — https://devblogs.nvidia.com/jetson-nano-ai-computing/

Very few results are present for the Coral Edge TPU board as it cannot run pre-trained models which were not trained with quantization aware training. In the above results Jetson used FP16 precision.

More results here — https://github.com/jolibrain/dd_performances

and https://www.phoronix.com/scan.php?page=article&item=nvidia-jetson-nano&num=3

Conclusion

In my opinion the Coral Edge TPU dev board is better because of the below reasons —

1. The Coral dev board at $149 is slightly expensive than the Jetson Nano ($99) however it supports Wifi and Bluetooth whereas for the Jetson Nano one has to buy an external wifi dongle.

2. Additionally the NXP iMX8 SOC on the coral board includes a Video processing unit and a Vivante GC700 lite GPU which can be used for traditional image and video processing. It also has a Cortex-M4F low power micro-controller which can be used to talk to other sensors like temperature sensor, ambient light sensor etc. More sensors here — http://lightsensors.blogspot.com/2014/09/collection-of-various-sensors.html

The Jetson also has Video encoder and decoder units. Additionally Jetson Nano has better support for other deep learning frameworks like Pytorch, MXNet. It also supports NVidia TensorRT accelerator library for FP16 inference and INT8 inference. Edge TPU board only supports 8-bit quantized Tensorflow lite models and you have to use quantization aware training.

I am interested in working on deep learning for edge applications, you can contact me for any interesting deep learning projects here.

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