{"product_id":"coral-dev-board-micro","title":"Coral Dev Board Micro","description":"\u003cmeta name=\"description\" content=\"Buy Coral Dev Board Micro online in India at best price from The Engineer Store, Bengaluru. Authentic product, 7-day warranty on manufacturing defects, fast delivery across India.\"\u003e\n\n\u003ch1\u003eCoral Dev Board Micro\u003c\/h1\u003e\n\n\u003cp\u003eThe Coral Dev Board Micro is a compact, ultra-low-power development board featuring Google's Edge TPU (Tensor Processing Unit) for accelerated machine learning inference at the edge. Machine learning engineers, IoT developers, and embedded systems professionals use this board to prototype and deploy AI models on resource-constrained devices with minimal latency and power consumption. It solves the critical problem of running sophisticated neural networks locally on microcontroller-grade hardware without relying on cloud connectivity, enabling real-time AI applications in robotics, computer vision, and autonomous systems.\u003c\/p\u003e\n\n\u003ch2\u003eProduct Overview\u003c\/h2\u003e\n\u003cp\u003eThe Coral Dev Board Micro integrates a quad-core Cortex-M7 processor running at 1GHz alongside Google's Edge TPU coprocessor, delivering hardware-accelerated machine learning inference specifically optimized for TensorFlow Lite models. The board operates at exceptionally low power consumption (under 100mW during typical inference), making it ideal for battery-powered and IoT applications where traditional GPU-based solutions are impractical. The Edge TPU accelerator provides up to 4 TOPS (Tera Operations Per Second) of INT8 performance, enabling real-time object detection, image classification, and pose estimation on edge devices without cloud dependency.\u003c\/p\u003e\n\n\u003cp\u003eThis development board features 1MB of SRAM and 8MB of flash memory, sufficient for deploying quantized neural networks directly on-device. The Micro variant represents Google's most compact offering in the Coral ecosystem, measuring just 1.5 x 2 inches, making it suitable for integration into space-constrained embedded systems. Connectivity is provided through USB-C for programming and power delivery, while the board supports standard debugging protocols including JTAG and serial interfaces. The reference implementation includes pre-trained models for common tasks, comprehensive Python and C++ SDKs, and extensive documentation enabling rapid prototyping of edge AI applications.\u003c\/p\u003e\n\n\u003ch2\u003eKey Specifications\u003c\/h2\u003e\n\u003ctable\u003e\n\u003ctr\u003e\n\u003ctd\u003eSpecification\u003c\/td\u003e\n\u003ctd\u003eDetails\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003eProduct Type\u003c\/td\u003e\n\u003ctd\u003eEdge AI Development Board with Integrated TPU\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003eBrand\u003c\/td\u003e\n\u003ctd\u003eGoogle Coral\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003eOrigin\u003c\/td\u003e\n\u003ctd\u003eOriginal\/Authentic\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003eWarranty\u003c\/td\u003e\n\u003ctd\u003e7 days on manufacturing defects\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003eShipping\u003c\/td\u003e\n\u003ctd\u003e1-5 days from Bengaluru\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003eDelivery\u003c\/td\u003e\n\u003ctd\u003e7-8 days across India\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003eSupport\u003c\/td\u003e\n\u003ctd\u003e24\/7 via Email and WhatsApp\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003eMain Processor\u003c\/td\u003e\n\u003ctd\u003eQuad-core ARM Cortex-M7 @ 1GHz\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003eEdge TPU\u003c\/td\u003e\n\u003ctd\u003eGoogle Edge TPU Coprocessor - 4 TOPS INT8\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003eMemory\u003c\/td\u003e\n\u003ctd\u003e1MB SRAM, 8MB Flash Storage\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003ePower Consumption\u003c\/td\u003e\n\u003ctd\u003eUnder 100mW during inference\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003eConnectivity\u003c\/td\u003e\n\u003ctd\u003eUSB-C (Power and Programming), JTAG, Serial Debug\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003eBoard Dimensions\u003c\/td\u003e\n\u003ctd\u003e1.5 x 2 inches (38.1 x 50.8 mm)\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003eOperating Temperature\u003c\/td\u003e\n\u003ctd\u003e0°C to 40°C\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003eSupported Models\u003c\/td\u003e\n\u003ctd\u003eTensorFlow Lite quantized models (INT8)\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003c\/table\u003e\n\n\u003ch2\u003eKey Features\u003c\/h2\u003e\n\u003cul\u003e\n\u003cli\u003eEdge TPU Acceleration: Dedicated hardware accelerator delivering 4 TOPS for TensorFlow Lite model inference without cloud dependency\u003c\/li\u003e\n\u003cli\u003eUltra-Low Power: Consumes under 100mW during typical inference operations, enabling battery-powered edge AI applications\u003c\/li\u003e\n\u003cli\u003eCompact Form Factor: Measures just 1.5 x 2 inches, ideal for integration into space-constrained IoT and robotics projects\u003c\/li\u003e\n\u003cli\u003eComplete Software Stack: Includes Python and C++ SDKs, pre-trained models, and comprehensive documentation for rapid development\u003c\/li\u003e\n\u003cli\u003eReal-Time Inference: Processes complex neural networks with minimal latency for time-critical applications like object detection and pose estimation\u003c\/li\u003e\n\u003cli\u003eDeveloper-Friendly: USB-C connectivity for seamless programming and debugging with standard JTAG and serial interfaces\u003c\/li\u003e\n\u003c\/ul\u003e\n\n\u003ch2\u003eApplications and Use Cases\u003c\/h2\u003e\n\u003cul\u003e\n\u003cli\u003eSmart Security Systems: Deploy real-time object detection and person recognition models directly on edge devices for surveillance without continuous cloud uploads\u003c\/li\u003e\n\u003cli\u003eRobotics and Autonomous Systems: Run lightweight computer vision models for navigation, obstacle detection, and gesture recognition on resource-constrained robots\u003c\/li\u003e\n\u003cli\u003eIndustrial IoT Monitoring: Implement predictive maintenance by running anomaly detection models on sensor data at the edge with minimal power overhead\u003c\/li\u003e\n\u003cli\u003eMobile and Wearable AI: Integrate advanced machine learning inference into battery-powered devices for on-device image classification and activity recognition\u003c\/li\u003e\n\u003cli\u003eAgricultural Technology: Deploy crop disease detection and pest identification models on field devices for real-time decision support without network connectivity\u003c\/li\u003e\n\u003cli\u003eMedical Devices: Run ECG analysis, vital sign monitoring, and diagnostic models on portable medical equipment with instant local processing\u003c\/li\u003e\n\u003c\/ul\u003e\n\n\u003ch2\u003eHow to Use\u003c\/h2\u003e\n\u003cp\u003eBegin by installing the Coral Dev Board Micro drivers and the TensorFlow Lite runtime environment on your development machine. Connect the board via USB-C to your computer, which provides both power and programming capability. Use the provided Python SDK to flash pre-trained models onto the board's 8MB flash storage, or compile your own quantized TensorFlow Lite models using the Edge TPU Compiler to optimize them for the hardware accelerator. The board supports standard debugging through serial interfaces, allowing you to monitor inference performance metrics and optimize model deployment in real-time.\u003c\/p\u003e\n\n\u003cp\u003eFor production deployment, leverage the C++ SDK to integrate the board into embedded systems with minimal overhead. The Edge TPU Compiler automatically handles model quantization and optimization, ensuring maximum throughput on the 4 TOPS accelerator. Use the provided example applications for object detection, image classification, and pose estimation as templates for your custom implementations. The comprehensive documentation includes performance benchmarking tools to validate inference latency and power consumption before final deployment, ensuring your edge AI solution meets real-world constraints.\u003c\/p\u003e\n\n\u003ch2\u003eFrequently Asked Questions\u003c\/h2\u003e\n\u003cdetails\u003e\n\u003csummary\u003eWhat is the difference between Coral Dev Board Micro and other Coral boards?\u003c\/summary\u003e\n\u003cp\u003eThe Coral Dev Board Micro is the most compact and power-efficient option in Google's Coral lineup, designed specifically for ultra-constrained IoT and embedded applications. Unlike the full-size Dev Board, the Micro variant sacrifices onboard RAM and storage expansion capabilities in favor of minimal power consumption and physical footprint. It is ideal for edge inference on pre-trained models, while the larger boards are better suited for model training and experimentation with larger neural networks.\u003c\/p\u003e\n\u003c\/details\u003e\n\u003cdetails\u003e\n\u003csummary\u003eCan I train models directly on the Coral Dev Board Micro?\u003c\/summary\u003e\n\u003cp\u003eNo, the Coral Dev Board Micro is optimized exclusively for inference, not training. You must train your models on a desktop GPU or cloud TPU, then quantize them to INT8 format using the Edge TPU Compiler before deploying to the board. This design choice enables the ultra-low power consumption and compact size that make the Micro variant ideal for production edge deployments.\u003c\/p\u003e\n\u003c\/details\u003e\n\u003cdetails\u003e\n\u003csummary\u003eWhat types of machine learning models are supported?\u003c\/summary\u003e\n\u003cp\u003eThe Coral Dev Board Micro supports TensorFlow Lite quantized models in INT8 format. Common supported architectures include MobileNet for image classification, SSD MobileNet for object detection, PoseNet for pose estimation, and custom neural networks that can be quantized and compiled using the Edge TPU Compiler. Float models must be quantized before deployment to leverage hardware acceleration.\u003c\/p\u003e\n\u003c\/details\u003e\n\u003cdetails\u003e\n\u003csummary\u003eHow much power does the Coral Dev Board Micro consume?\u003c\/summary\u003e\n\u003cp\u003eThe board consumes under 100mW during typical inference operations and approximately 10-20mW in idle states. Actual power consumption depends on model complexity, inference frequency, and USB power delivery efficiency. This ultra-low power profile makes it suitable for battery-powered applications requiring continuous or frequent edge AI processing.\u003c\/p\u003e\n\u003c\/details\u003e\n\u003cdetails\u003e\n\u003csummary\u003eIs the Coral Dev Board Micro suitable for production deployment?\u003c\/summary\u003e\n\u003cp\u003eYes, the Coral Dev Board Micro is production-ready for edge inference applications. However, for high-volume manufacturing, consider the Coral System-on-Module (SoM) variant, which provides the same Edge TPU acceleration in a smaller form factor optimized for integration into custom PCBs. The Dev Board is excellent for prototyping and low-to-medium volume deployments.\u003c\/p\u003e\n\u003c\/details\u003e\n\u003cdetails\u003e\n\u003csummary\u003eWhen will I receive my order?\u003c\/summary\u003e\n\u003cp\u003eOrders are dispatched within 1-5 business days from our Bengaluru warehouse. Delivery takes 7-8 days to most locations across India.\u003c\/p\u003e\n\u003c\/details\u003e\n\u003cdetails\u003e\n\u003csummary\u003eWhat is your return and warranty policy?\u003c\/summary\u003e\n\u003cp\u003eWe offer a 7-day return policy on manufacturing defects only. Contact support within 7 days of receipt for free replacement or full refund. Not applicable for user damage or misuse.\u003c\/p\u003e\n\u003c\/details\u003e\n\u003cdetails\u003e\n\u003csummary\u003eAre bulk discounts available?\u003c\/summary\u003e\n\u003cp\u003eYes, wholesale pricing for orders of 10 or more units. Contact our sales team via WhatsApp or email for a customized bulk quote.\u003c\/p\u003e\n\u003c\/details\u003e\n\n\u003ch2\u003eWhy Buy from The Engineer Store\u003c\/h2\u003e\n\u003cul\u003e\n\u003cli\u003eGenuine Products: Sourced directly from authorized distributors with authentication\u003c\/li\u003e\n\u003cli\u003eExpert Team: Our technical team validates every product before listing\u003c\/li\u003e\n\u003cli\u003eFast Shipping: Dispatched within 1-5 days from our Bengaluru warehouse\u003c\/li\u003e\n\u003cli\u003ePan-India Delivery: 7-8 days to Mumbai, Delhi, Chennai, Hyderabad, Pune, Kolkata\u003c\/li\u003e\n\u003cli\u003ePayment Options: COD, UPI, credit\/debit cards, net banking, EMI available\u003c\/li\u003e\n\u003cli\u003eTechnical Support: 24\/7 expert guidance via email and WhatsApp\u003c\/li\u003e\n\u003cli\u003eReturns: 7-day return policy on manufacturing defects only\u003c\/li\u003e\n\u003c\/ul\u003e\n\n\u003ch2\u003eBuy Coral Dev Board Micro Online in India\u003c\/h2\u003e\n\u003cp\u003ePurchase the \u003cstrong\u003eCoral Dev Board Micro\u003c\/strong\u003e online at \u003ca href=\"https:\/\/theengineerstore.in\"\u003eThe Engineer Store\u003c\/a\u003e, India's trusted source for genuine electronics. We deliver across Bengaluru, Mumbai, Delhi, Chennai, Hyderabad, Pune, Kolkata, Ahmedabad, Jaipur, and Surat. Get the best price on \u003cstrong\u003eCoral Dev Board Micro\u003c\/strong\u003e with fast shipping and expert support.\u003c\/p\u003e\n\u003cp\u003eOur team in Bengaluru is available 24\/7 to support your journey from product selection to project completion.\u003c\/p\u003e","brand":"My Store","offers":[{"title":"Default Title","offer_id":43847451312291,"sku":"TES-EV00006107","price":11074.99,"currency_code":"INR","in_stock":true}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/0628\/4479\/7091\/products\/Coral-Dev-Board-Micro-2-314x252.jpg?v=1703853897","url":"https:\/\/www.theengineerstore.in\/zh-hant\/products\/coral-dev-board-micro","provider":"The Engineer Store","version":"1.0","type":"link"}