{"product_id":"sipeed-maix-i-module","title":"Sipeed MAIX-I module","description":"\u003cmeta name=\"description\" content=\"Buy Sipeed MAIX-I module 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\u003eSipeed MAIX-I module\u003c\/h1\u003e\n\n\u003cp\u003eThe Sipeed MAIX-I module is a compact AI accelerator board featuring the K210 dual-core RISC-V processor with built-in neural network accelerator, designed for edge machine learning and computer vision applications. Professional developers, roboticists, and embedded systems engineers use this module to deploy real-time object detection, facial recognition, and image classification models directly on resource-constrained IoT devices. It solves the critical challenge of running sophisticated AI inference at the edge without requiring cloud connectivity, enabling low-latency, privacy-preserving intelligent applications in industrial automation, surveillance, and autonomous systems.\u003c\/p\u003e\n\n\u003ch2\u003eProduct Overview\u003c\/h2\u003e\n\u003cp\u003eThe Sipeed MAIX-I module integrates the Kendryte K210 SoC, a purpose-built AI processor with dual 400MHz RISC-V cores and a dedicated neural network accelerator capable of 1 TOPS peak performance. The architecture combines general-purpose computing with specialized hardware for convolutional neural networks, allowing simultaneous execution of AI inference and control logic. The module operates at exceptionally low power consumption (approximately 0.3W in active mode), making it ideal for battery-powered and embedded applications where thermal management and energy efficiency are paramount concerns.\u003c\/p\u003e\n\n\u003cp\u003eWhat distinguishes the MAIX-I from generic microcontroller boards is its optimized memory hierarchy featuring 8MB on-chip SRAM and support for external PSRAM expansion, coupled with dedicated hardware for image preprocessing and neural network quantization. The module supports MobileNet, SqueezeNet, and other lightweight neural architectures pre-optimized for the K210's accelerator. Integration with the Kendryte nncase compiler enables seamless conversion of TensorFlow and ONNX models to K210-optimized binaries, eliminating the complexity of manual model optimization and quantization workflows.\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\u003eAI Accelerator Module with Embedded Neural Network Processor\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003eBrand\u003c\/td\u003e\n\u003ctd\u003eSipeed\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\u003eProcessor\u003c\/td\u003e\n\u003ctd\u003eKendryte K210 Dual-Core RISC-V at 400MHz\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003eNeural Network Accelerator\u003c\/td\u003e\n\u003ctd\u003e1 TOPS Peak Performance, INT8 Quantization Support\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003eOn-Chip Memory\u003c\/td\u003e\n\u003ctd\u003e8MB SRAM, 2MB ROM\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003ePower Consumption\u003c\/td\u003e\n\u003ctd\u003e0.3W Active Mode, 50mW Sleep Mode\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003eOperating Voltage\u003c\/td\u003e\n\u003ctd\u003e3.3V to 5V with integrated voltage regulator\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003eInterfaces\u003c\/td\u003e\n\u003ctd\u003eUART, SPI, I2C, GPIO, DVP Camera Interface\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003c\/table\u003e\n\n\u003ch2\u003eKey Features\u003c\/h2\u003e\n\u003cul\u003e\n\u003cli\u003eDedicated Neural Network Accelerator delivering 1 TOPS peak throughput for real-time CNN inference without CPU bottlenecks\u003c\/li\u003e\n\u003cli\u003eDual-core RISC-V processor architecture enabling parallel execution of AI models and control logic simultaneously\u003c\/li\u003e\n\u003cli\u003e8MB on-chip SRAM with configurable cache hierarchy optimized for convolutional operations and feature map storage\u003c\/li\u003e\n\u003cli\u003eIntegrated DVP camera interface supporting OV2640 and OV7740 sensors for direct image capture and preprocessing\u003c\/li\u003e\n\u003cli\u003eUltra-low power consumption enabling deployment in battery-powered IoT and edge devices with extended runtime\u003c\/li\u003e\n\u003cli\u003eSupport for MobileNet, SqueezeNet, and custom quantized models through nncase compiler toolchain\u003c\/li\u003e\n\u003cli\u003eHardware-accelerated image preprocessing including scaling, rotation, and color space conversion\u003c\/li\u003e\n\u003cli\u003eFlexible memory configuration with external PSRAM support up to 32MB for larger model deployment\u003c\/li\u003e\n\u003c\/ul\u003e\n\n\u003ch2\u003eApplications and Use Cases\u003c\/h2\u003e\n\u003cul\u003e\n\u003cli\u003eFace Detection and Recognition in smart surveillance systems and access control terminals requiring local processing without cloud dependency\u003c\/li\u003e\n\u003cli\u003eObject Detection and Tracking in autonomous robotics and industrial inspection systems for real-time visual feedback\u003c\/li\u003e\n\u003cli\u003eGesture Recognition in human-computer interaction applications and smart home control interfaces\u003c\/li\u003e\n\u003cli\u003eLicense Plate Recognition and Vehicle Classification in traffic monitoring and parking management systems\u003c\/li\u003e\n\u003cli\u003eAnomaly Detection in manufacturing quality control where edge processing reduces latency and bandwidth requirements\u003c\/li\u003e\n\u003cli\u003ePose Estimation in fitness tracking and ergonomic monitoring applications for wearable devices\u003c\/li\u003e\n\u003c\/ul\u003e\n\n\u003ch2\u003eHow to Use\u003c\/h2\u003e\n\u003cp\u003eBegin by connecting the MAIX-I module to your development machine via USB using the integrated CH340 serial interface. Install the Kendryte IDE or use the open-source PlatformIO framework with the K210 platform package. Flash the firmware using kflash utility, then configure the camera interface by selecting appropriate sensor drivers for your connected camera module. The module boots into MicroPython REPL by default, allowing interactive testing of image capture and preprocessing pipelines before deploying compiled neural network models.\u003c\/p\u003e\n\n\u003cp\u003eFor neural network deployment, convert your trained model using the nncase compiler with appropriate quantization settings for INT8 precision. Load the compiled model binary into the module's memory space, then initialize the neural network accelerator through the provided APIs. Configure the DVP camera parameters including resolution, frame rate, and color format to match your model's input requirements. Execute inference loops by capturing frames, preprocessing through hardware accelerators, and retrieving predictions from the neural network engine, all while maintaining deterministic timing for real-time applications.\u003c\/p\u003e\n\n\u003ch2\u003eFrequently Asked Questions\u003c\/h2\u003e\n\u003cdetails\u003e\n\u003csummary\u003eWhat is the maximum resolution and frame rate for camera input on the MAIX-I module?\u003c\/summary\u003e\n\u003cp\u003eThe DVP camera interface supports up to 2MP resolution at 30 FPS, with common configurations being 640x480 at 30 FPS or 320x240 at 60 FPS. The actual achievable frame rate depends on the neural network model complexity and preprocessing operations. For lightweight models like MobileNet, you can achieve 15-20 FPS at VGA resolution with full preprocessing pipeline enabled.\u003c\/p\u003e\n\u003c\/details\u003e\n\u003cdetails\u003e\n\u003csummary\u003eCan I run multiple neural network models sequentially or in parallel on the MAIX-I?\u003c\/summary\u003e\n\u003cp\u003eThe MAIX-I supports sequential model execution where you load different models into memory and switch between them. Parallel execution is limited by the 8MB on-chip SRAM capacity. For applications requiring multiple models, use the dual-core architecture where one core handles camera I\/O and preprocessing while the other executes inference, or implement time-multiplexed inference switching between models based on application logic.\u003c\/p\u003e\n\u003c\/details\u003e\n\u003cdetails\u003e\n\u003csummary\u003eWhat neural network model sizes and architectures are compatible with the K210 accelerator?\u003c\/summary\u003e\n\u003cp\u003eThe K210 accelerator optimizes models up to 5-10MB in size with INT8 quantization. Compatible architectures include MobileNetV1\/V2, SqueezeNet, YOLOv2-Tiny, and custom CNNs with depthwise separable convolutions. Models must be quantized to INT8 precision using nncase compiler. Larger models can be stored in external PSRAM but will have reduced inference speed due to memory bandwidth limitations compared to on-chip SRAM execution.\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 Sipeed MAIX-I module Online in India\u003c\/h2\u003e\n\u003cp\u003ePurchase the \u003cstrong\u003eSipeed MAIX-I module\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\u003eSipeed MAIX-I module\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":43856801202339,"sku":"TES-EV00082032","price":1086.5,"currency_code":"INR","in_stock":true}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/0628\/4479\/7091\/products\/Maix_201-228x228.jpg?v=1704280691","url":"https:\/\/www.theengineerstore.in\/zh-hans\/products\/sipeed-maix-i-module","provider":"The Engineer Store","version":"1.0","type":"link"}