Sipeed
sipeed maixduino kit for risc-v ai + iot give 200 eord
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₹4,438
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1 Year Warranty

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Ships from | elemart |
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Sold by | davesh |
About this item
The Sipeed Maixduino Kit for RISC‑V AI + IoT is a compact, Arduino‑compatible development platform powered by a RISC‑V 64-bit dual-core processor with integrated FPU, optimized for AI-driven edge applications. Clocked at 400 MHz and even capable of overclocking, this module delivers ample performance for neural network inference and machine vision tasks. Designed in the familiar Arduino Uno form factor, it integrates an ESP32 module that brings Wi‑Fi and Bluetooth 4.2 connectivity, enabling seamless IoT integration. The kit includes intelligent peripherals such as an omnidirectional I²S MEMS microphone for audio capture, and drivers for convolutional neural network–based vision—handling QVGA at 60 fps or VGA at 30 fps. It supports deep learning frameworks like Tiny‑YOLO, MobileNet, and TensorFlow Lite, and is compatible with popular development environments including MaixPy, Arduino IDE, OpenMV, and PlatformIO. For expansion and multimedia, it provides connectors for DVP cameras and 8‑bit MCU LCDs, a micro‑SD card slot for storage, plus a 3 W DAC + PA audio output. The board powers up through USB‑C and is ideal for use in smart home, education, healthcare, agriculture, and industrial AIoT applications. More details here: [Seeed Studio product page]Specifications
Processor & AI | RISC‑V dual‑core 64‑bit with FPU, 400 MHz neural network processor (overclockable |
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Memory | 8 MB on‑chip SRAM |
Connectivity | Onboard ESP32 module with Wi‑Fi (802.11 b/g/n) and Bluetooth 4.2 |
Vision Processing | QVGA at 60 fps or VGA at 30 fps image recognition, convolutional neural network support |
Interfaces | I²S MEMS microphone, DVP camera connector (24-pin), 8‑bit MCU LCD connector (24-pin), micro‑SD card slot |
Audio Output | Built‑in 3 W DAC + PA amplifier, plus microphone array support |
Software Support | Compatible with MaixPy IDE, Arduino IDE, OpenMV IDE, PlatformIO, and supports Tiny‑YOLO, MobileNet, TensorFlow Lite |
Power & Form Factor | USB‑C power input (6–12 V via DC‑DC step‑down, providing 5 V at 1.2 A), Arduino Uno footprint |