Sipeed MAix Go have on board JTAG&UART based on STM32F103C8, I2S Mic, Speaker, RGB LED, Mic array connector, thumbwheel, TF card Slot and lithium battery manager chip with power path management function, all pins out, with standard M12 lens DVP camera.
Who Viewed This Also Viewed
Sipeed MAix: AI at the edge
AI is pervasive today, from consumer to enterprise applications. With the explosive growth of connected devices, combined with a demand for privacy/confidentiality, low latency and bandwidth constraints, AI models trained in the cloud increasingly need to be run at the edge.
MAIX is Sipeed’s purpose-built module designed to run AI at the edge, we called it AIoT. It delivers high performance in a small physical and power footprint, enabling the deployment of high-accuracy AI at the edge, and the competitive price make it possible embed to any IoT devices. As you see, Sipeed MAIX is quite like Google edge TPU, but it act as master controller, not an accelerator like edge TPU, so it is more low cost and low power than AP+edge TPU solution.
MAix's Advantage and Usage Scenarios:
Inherit the advantage of K210's small footprint, Sipeed MAIX-I module, or called M1, integrate K210, 3-channel DC-DC power, 8MB/16MB/128MB Flash (M1w module add wifi chip esp8285 on it) into Square Inch Module. All usable IO breaks out as 1.27mm(50mil) pins, and pin's voltage is selectable from 3.3V and 1.8V.
Sipeed MAix Go development kit
MAix Go is bigger and better than M1 Dock.
MAIX support original standalone SDK and FreeRTOS SDK base on C/C++.And it is also compatible with micropython which has many basic libraries for developing such as FPIOA, GPIO, TIMER, PWM, Flash, OV2640, LCD, etc. Besides, it can support zmodem protocol, SPIFFS library for wireless communication. you can use python or vi to edit the code to the board.
MAix's Deep learning
MAIX support fixed-point model that the mainstream training framework trains, according to specific restriction rules, and have model compiler to compile models to its own model format.It support tiny-yolo, mobilenet-v1, and, TensorFlow Lite! Many TensorFlow Lite model can be compiled and run on MAIX! And We will soon release model shop, you can trade your model on it.