Coral Dev Board Micro

The Coral Dev Board Micro is a microcontroller board with a built-in camera, microphone, and Coral Edge TPU, allowing you to quickly prototype and deploy low-power embedded systems with on-device ML inferencing. By combining the Cortex M4 and M7 processors with the Coral Edge TPU on this board, you can design systems that gracefully cascade from extremely low-power ML inferencing to more complexyet still power-efficientML inferencing. The Coral Dev Board Micro is a microcontroller board with a dual-core MCU, Coral Edge TPU, camera, and microphone. With this board, you can build low-power systems with fast on-device inferencing for vision and audio ML applications. You can also expand the hardware with custom add-on boards using high-density board-to-board connectors. The Edge TPU is a small ASIC designed by Google that accelerates TensorFlow Lite models in a power-efficient manner. One Edge TPU is capable of performing 4 trillion operations per second (4 TOPS). This on-device ML processing reduces latency, increases data privacy, and removes the need for a constant internet connection. Overall, the Coral Dev Board Micro is a powerful and versatile board that can be used for a wide range of AI applications at the edge. Its compact size and low power consumption make it a great choice for embedded systems and other devices where space is limited. Features: Processor: The Coral Dev Board Micro is powered by the Google Edge TPU, which is a machine learning accelerator that can perform up to 4 trillion operations per second (TOPS). Memory: It has 2GB of LPDDR4 RAM and 8GB of eMMC storage. Connectivity: The board supports Wi-Fi and Bluetooth connectivity, and has a USB-C port for power and data transfer. Ports: It has a variety of ports, including a micro HDMI port, a MIPI-CSI camera port, and a 40-pin GPIO header. Size: The Coral Dev Board Micro measures just 27mm x 42mm, making it small enough to fit in a variety of devices. Software: The board comes preloaded with the Mendel Linux operating system, which is based on Debian Linux, and supports TensorFlow Lite and AutoML Vision Edge software. Power consumption: The board has a low power consumption, which makes it ideal for battery-powered devices.