Google Coral Dev Board - Jetson Nano - Raspberry pi 4 (4GB) - for Artificial Intelligence and Machine Learning

Synergy - an important term in Mechatronics or Artificial Intelligence. Sync between energies - both Hardware and Software is the roots of building an AI product.

In this way forward, Google Launched its Coral dev board named for Machine Learning and Artificial Intelligence with TensorFlow support.

The Board actually made with design structure of Raspberry Pi with SOM(system on module). Linux version - Mendel Debian is used as Operating system.

The TPU(Tensor Processing Unit) is a coprocessor optimized for handling neural networks, intended to push artificial intelligence out from centralized clouds to embedded devices.

The TPU is designed for the performance phase, when systems with compiled models are presented with real-world data and are expected to behave appropriately, using a version of TensorFlow called TensorFlow Lite.

The Dev Board is designed to make hardware experimentation easy, with a Pi-like general-purpose input/output (GPIO) connector, SD-card reader, HMDI video output, a Wi-Fi radio, an Ethernet port, a port for attaching a camera module, and a USB port for peripherals. Like the Pi 3, it has 1 gigabyte (GB)of RAM and uses an Arm-based processor as its CPU.

There are few speculations of Running Android on Coral board as TensorFlow is supported on Android will be a step further into the AI world.

Cost of Google Coral TPU - $150 / Rs,11,000 approx
To know more about Tensorflow - check out here

But, this has also been a reason for Android aarch64 version is not made open-sourced.

"Google's New Hardware line towards Artificial Intelligence"
 The next one is Jetson Nano,

NVIDIA - a graphics processor company's hardware - Jetson Nano. In reality, graphics mimic more video or pictorial way.

Machine Learning is all about Dimensions if you are not aware - please find TensorFlow links.
In that way, NVIDIA opts for its own hardware for real-time AI projects.

The Jetson Nano - takes machine learning seriously. It boasts of an Nvidia Maxwell 128 CUDA core GPU that is optimized for machine learning. This offers 472 GFLOPS for AI performance as opposed to the 21.4 GFLOPs you get from Raspberry Pi model 3B+.

Powering the GPU is a higher performance 64-bit Quad-core Cortex A57 CPU and a whopping 4GB RAM.

When it comes to video processing, the AI functions of the Jetson Nano come to light. The Jetson nano can process 4K videos using the onboard hardware for encoding, decode, and display.

This board can run parallel neural networks to process multiple videos and sensors simultaneously. It can process multiple video streams, up to eight 1080p video feeds at a time (picture drones with multiple lenses), and uses machine-learning algorithms to detect and track images.

Cost - 99$ / Rs.7000 approx

It runs ubuntu natively, hence software support won't be an issue as currently most rendering AI projects are found on Tensorflow which supports ubuntu.

on the power of CPU comparison, this board leads both TPU and raspberry pi 4.

The Final one, Raspberry Pi 4(4GB)

Raspberry Pi is a product of hit, so it is very usual to follow their line but 4GB variant proved they too joined the AI race.


  • CPU: 4x ARM Cortex-A72, 1.5GHz
  • Ports: 2 x micro-HDMI, 2 USB 3.0, 2 USB 2.0, two-lane MIPI Camera Serial Interface (CSI), two-lane MIPI Display Serial Interface (DSI), 3.5mm analog audio-video jack

The major advantage of Raspberry pi is the developer's community in various fields of AI, IoT, Machine Learning who have already done projects.

Even Raspberry Pi3 tested with Tensorflow for object detection but now with Hardware Acceleration few object detection models of Google Coral repo's has been already tested.

Operating System is not a problem for Raspberry pi as an official ubuntu mate will also be available soon.

Things to ponder, Jetson Nano is 10% better suited as GFLOPS difference showed. But, considering the cost and developer support around the globe.

Even Chromium OS is currently under testing for rpi4 and also TensorFlow lite official support extends the raspberry pi to lead in between these two boards.

Cost:$55/Rs. 4000 approx