Is the Google Coral Dev Board Better Than a Raspberry Pi?

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Is the Google Coral Dev Board Better Than a Raspberry Pi?

Single-board computers (SBCs) are game changers. The Raspberry Pi is unquestionably the most well-known SBC. It’s small, about the size of a credit card, and performs most of the same duties as a desktop computer, making it a popular choice among enthusiasts and developers both.

The Coral Dev Board is a newcomer to the scene. There are several reasons to be enthusiastic about it. You’ll learn all you need to know about Google’s new Linux-based SBC today.

What Is the Google Coral Dev Board?

The Coral Dev board is a single-board computer (SBC) that runs Google’s own Mendel operating system and is intended for usage with the TensorFlow Lite neural network. It also features an abundance of General Purpose In/Out (GPIO) pins. You would believe this is another Raspberry Pi clone based on its appearance and size, but there are several key distinctions.

What Makes the Coral Dev Board Unique?

The Coral Dev Board was created by Google for rapid development of machine learning hardware. The Edge TPU Module is the first item that distinguishes it.

This module, known as a System On Module (SOM), fits on top of a connected baseboard and includes all of the board’s components. The CPU, GPU, RAM, Wi-Fi chip, and flash memory are all housed in a single modular unit that can be replaced out rapidly.

The baseboard has USB, LAN, HDMI, SD card, audio, and power ports. This implies you may use a single baseboard to prototype devices before deploying them on specialized hardware.

It also means that technology with real-world commercial uses is accessible to those who wish to tinker with the technology at home. What really sets it aside is something you wont find on any other development board: a Google Edge TPU co-processor.

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What Is the Google Edge TPU Co-Processor?

The Edge TPU is a small Application Specific Integrated Chip (ASIC) engineered to deliver high-performance machine learning interpretation. Its tiny size and low power requirements make it perfect for embedding into IoT hardware products for image and text recognition.

The addition of this chip to an SBC drastically reduces the time a neural network requires to process data. Currently, the Edge TPU is only available as part of the Coral Dev Board, or as an external USB processor.

What Are the Coral Dev Board’s Specs?

The specifications for the Edge TPU Module are as follows:

  • NXP i.MX 8M SOC CPU (quad Cortex-A53, Cortex-M4F)
  • GPU: GC7000 Lite Graphics Integrated
  • Coprocessor: Google Edge TPU
  • Flash memory: 8GB eMMC
  • Bluetooth 4.1, Wi-Fi 2×2 MIMO (802.11b/g/n/ac 2.4/5GHz)
  • Dimensions: 48 x 40 x 5mm

The baseboard has its own set of specifications:

  • Flash memory: MicroSD
  • Type-C USB OTG Type-C power Micro-B serial console Type-A 3.0 host
  • LAN: Gigabit Ethernet port
  • Audio input: 3.5mm audio jack (CTIA compliant)Digital PDM microphone (x2) 2.54mm 4-pin stereo speaker terminal
  • HDMI 2.0a video (full size)MIPI-DSI display 39-pin FFC connection (4-lane)MIPI-CSI2 camera 24-pin FFC connection (4-lane)
  • GPIO: 3.3V power rail, configurable impedance of 40-255 ohms, maximum current of 82 mA
  • Power: 5V DC (USB Type-C)
  • Dimensions: 88 x 60 x 24mm

Python is the only currently supported programming language. C++ support is coming soon.

As it’s design supports complex data processing, it is among the most powerful of the Raspberry Pi alternatives. What it lacks, however, is a desktop operating system. The documentation for the Coral Dev Board even advises not connecting a monitor and keyboard to the board and only using network connection solutions such as SSH.

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What Can the Coral Dev Board Do?

Specifications don’t mean anything if the hardware isn’t useful. The above video shows just how powerful the Coral Dev Board can be. The example uses a camera module to introduce new objects to a TensorFlow Lite neural network in real time.

This example is likely to satisfy the DIY crowd, but there are other TensorFlow Lite applications you may be acquainted with. Google just released Smart Reply for Gmail, which reads email and provides context-sensitive responses using the same lightweight version of TensorFlow.

This kind of smart technology often requires an internet connection or the usage of third-party services, which might pose a security risk. Neural networks may be employed offline and in regions where an internet connection is not feasible by harnessing the capability of on-board machine learning.

In summary, the Coral Dev Board is the first significant step toward strong tiny form factor machine learning. The swappable SOM makes mass production simple, and this technology will undoubtedly be found in many future gadgets. What does this have to do with you?

Will the Coral Dev Board Replace My Raspberry Pi?

TensorFlow can be run on a Raspberry Pi, although it is not meant for it. The Coral Dev Board provides power that the Pi just cannot match. Fortunately, a Raspberry Pi compatible USB Accelerator with an embedded Edge TPU processor is available as a solution.

TensorFlow Lite may now be used by anybody with a Raspberry Pi and basic machine learning knowledge.

Where to Buy the Coral Dev Board

Surely this new tech is too expensive for hobbyists?

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No, surprisingly. The Coral Dev Board costs $149.99, which is comparable to the price of similar high-end development boards. The USB Accelerator is even less expensive, making it an excellent choice for individuals who do not want to invest in a new development board. The SOM and a PCI TPU acceleration card are planned to be sold separately, however they are presently unavailable for purchase.

You can pick up the components from Google’s Coral website:

The New Hotness

The Coral Dev board, as fascinating as it is, will not replace the Raspberry Pi. TensorFlow can run on a Raspberry Pi, but not in real time. The Coral Dev Board is a powerful tool, but it is not intended for desktop usage or instruction.

The Raspberry Pi remains the ideal option for beginning electronics enthusiasts looking to create amazing DIY Raspberry Pi projects.

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