.png)
Category: Coral
Google Coral
The Google Coral category includes advanced modules and accessories for machine learning projects. These products are characterized by high performance, compact sizes, and a wide range of functions, making them ideal for both professionals and electronics enthusiasts.
There are 11 products.
The Coral USB Accelerator adds an Edge TPU coprocessor to your system, enabling high-speed machine learning inferencing on a wide range of systems, simply by connecting it to a USB port. Coral USB Accelerator G950-06809-01
M.2 module that allows the Edge TPU coprocessor to be used in existing systems. It allows you to make modern mobile vision models in a power efficient manner. Coral M.2 Accelerator A+E key G650-04527-01
ML accelerator module that allows the use of the Edge TPU coprocessor in existing systems. It will be used in projects using machine learning and artificial intelligence. Coral M.2 Accelerator with Dual Edge TPU G650-06076-01
PCIe module that allows the Edge TPU coprocessor to be used in existing systems. It allows you to make modern mobile vision models in a power efficient manner. Coral Mini PCIe Accelerator G650-04528-01
Surface mount module with Edge TPU coprocessor. Supports TensorFlow Lite models. Coral G313-06329-00
Add-on board (also known as a pHAT or bonnet) that adds sensing capabilities to your Coral Dev Board or Raspberry Pi projects. The board provides atmospheric data such as light level, barometric pressure, temperature, and humidity. Coral Environmental Sensor Board
M.2 module that allows the Edge TPU coprocessor to be used in existing systems. It allows you to make modern mobile vision models in a power efficient manner. Coral G650-04686-01
Ethernet module dedicated to Coral Dev Board Micro. It supports power over Ethernet (PoE) technology and connects to the base board via B2B connectors. Coral G650-07975-01
Single-board computer with SoC MediaTek 8167s chip, designed for use in machine learning. Supports TensorFlow Lite models. Coral Dev Board Mini
Development board with i.MX RT1176 dual-core microcontroller, Coral Edge TPU, camera and microphone. Designed for artificial intelligence applications using machine learning for image and speech processing. Coral G650-07968-01
A kit containing two dedicated enclosures for Coral Dev Board Micro. Made of plastic, it provides protection from mechanical damage for Coral Dev Board Micro kits with Wireless Add-on and PoE Add-On. Coral G730-06370-01
Powerful ML Accelerator Modules
Coral M.2 Accelerator with Dual Edge TPU is an ML accelerator module that provides exceptional computing power, enabling faster data processing in machine learning projects. Coral Accelerator Module with Edge TPU (SMT) offers high performance in a compact form, ideal for integrated systems where space is limited but performance must be top-notch.
Wireless Connectivity for Coral Boards
Coral Wireless Add-on is a WiFi module designed specifically for Coral Dev Board Micro. It allows easy and fast addition of wireless connectivity to IoT projects and embedded systems that require stable and fast network connections. This significantly increases the functionality and range of applications for these devices.
Versatile Single-Board Computers
Coral Dev Board Mini is a compact single-board computer featuring the MediaTek 8167s SoC, providing high performance and versatility in projects. Ideal for complex applications where both computing power and compact dimensions are essential. Coral Dev Board 4GB, with the NXP i.MX 8M chip, offers additional computing power and memory, making it perfect for more demanding projects where a stable and efficient computing platform is necessary.
Comprehensive Range of Google Coral Products at Kamami
Kamami offers a wide selection of Google Coral products, including accelerator modules, single-board computers, WiFi modules, and various accessories. With these, every electronics enthusiast and professional can find high-quality components to help realize even the most advanced and demanding projects related to machine learning and embedded systems.