The ODROID-C0 is a computer for those who wish to make a more flexible and portable applications. It is a minimized hardware version of the ODROID-C1+. Battery power circuit is fully integrated. Just attach a 3.7V Li+ battery and make it mobile. It is highly suitable for IoT projects, wearables, and other applications that require a lightweight device.
All the ODROID-C1/C1+ OS images are fully compatible with the ODROID-C0. Some of the modern operating systems that run on the ODROID-C0 are Ubuntu, Android, Arch Linux, Debian, and OpenELEC, with thousands of free open-source software packages available. There are also plenty of custom OS available for using the ODROID-C0 as a multimedia center, Kodi, gaming station, headless server and much more at our ODROID community.
The key features and improvements over the original ODROID-C1+:
* Amlogic ARM® Cortex®-A5(ARMv7) 1.5Ghz quad core CPUs * Mali™-450 MP2 GPU (OpenGL ES 2.0/1.1 enabled for Linux and Android) * 1Gbyte DDR3 SDRAM * eMMC4.5 HS200 Flash Storage slot / UHS-1 SDR50 MicroSD Card slot * 40pin + 7pin GPIOs (unpopulated) * USB 2.0 Host x 2 (unpopulated) * Infrared(IR) Receiver (unpopulated) * Li+ rechargeable battery charger for wearable and robots application * Battery voltage level is accessible via ADC in the SoC. * DC/DC step-down converters for higher power efficiency * DC/DC step-up converter for 5Volt rails (USB host and HDMI) from a Li-Polymer battery * DIY friendly C0 Connector Pack is available for handy prototypin
ODROID-C0
Data sheet
Board Category
ODROID-C0
* Amlogic ARM® Cortex®-A5(ARMv7) 1.5Ghz quad core CPUs * Mali™-450 MP2 GPU (OpenGL ES 2.0/1.1 enabled for Linux and Android) * 1Gbyte DDR3 SDRAM * eMMC4.5 HS200 Flash Storage slot / UHS-1 SDR50 MicroSD Card slot
This MIPI camera kit has an OV5647 sensor chip which can capture up to 5 Mpixel images and 720p/30fps video streaming. You can get reasonably good imaging whether it is in the daytime or at night thanks to the automatically switchable IR-Cut filter and two IR Lights. Realtime image inputs from the MIPI-CSI port can be widely used for Machine-Learning applications.
This “Case Type 1” is designed for the user who needs high bandwidth network with the high capacity storages for the high performance computing system.