{"product_id":"m1w-ai-lot-module-k210-deep-learning","title":"M1W AI+lOT Module K210 Deep learning","description":"\u003cmeta name=\"description\" content=\"Buy M1W AI+lOT Module K210 Deep learning 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\u003eM1W AI+lOT Module K210 Deep learning\u003c\/h1\u003e\n\n\u003cp\u003eThe M1W AI+lOT Module K210 is a specialized edge computing accelerator designed for real-time deep learning inference and IoT applications, featuring a dual-core 64-bit RISC-V processor with dedicated neural network acceleration hardware. Professional embedded systems engineers, roboticists, and IoT developers use this module to deploy machine learning models directly on edge devices without relying on cloud infrastructure, achieving sub-100ms inference latencies for computer vision and audio processing tasks. This module solves the critical challenge of running sophisticated AI algorithms on resource-constrained devices while maintaining low power consumption, high reliability, and complete offline operation capability.\u003c\/p\u003e\n\n\u003ch2\u003eProduct Overview\u003c\/h2\u003e\n\u003cp\u003eThe K210 module integrates a powerful dual-core 64-bit RISC-V processor running at 400MHz with a dedicated neural network accelerator (KPU) capable of processing convolutional neural networks at exceptional speeds. The architecture employs a specialized instruction set for quantized neural networks, supporting INT8 and INT16 operations, which dramatically reduces memory bandwidth requirements and computational overhead compared to floating-point implementations. The module features 8MB of SRAM for model caching and 6MB of general-purpose RAM, enabling deployment of moderately complex models including MobileNet, YOLOv2, and custom CNN architectures without external memory dependencies.\u003c\/p\u003e\n\n\u003cp\u003eWhat distinguishes the K210 from generic microcontrollers is its hardware-accelerated KPU co-processor, which can execute convolutional layers at rates exceeding 0.3 TFLOPS for INT8 operations, delivering 10-50x performance improvements over software-based implementations. The module integrates dual-channel audio input with onboard microphones, DVP camera interface supporting OV2640 and OV5640 sensors, and comprehensive peripheral support including SPI, I2C, UART, and GPIO. Power efficiency is exceptional, consuming as little as 0.3W during inference operations, making it ideal for battery-powered robotics, surveillance systems, and always-on IoT edge devices.\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+IoT Edge Computing Module\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003eBrand\u003c\/td\u003e\n\u003ctd\u003eSipeed K210\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\u003eDual-core 64-bit RISC-V @ 400MHz\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003eNeural Network Accelerator\u003c\/td\u003e\n\u003ctd\u003eKPU co-processor, 0.3+ TFLOPS INT8\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003eMemory\u003c\/td\u003e\n\u003ctd\u003e8MB SRAM + 6MB General RAM\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003eSupported Data Types\u003c\/td\u003e\n\u003ctd\u003eINT8, INT16 quantized neural networks\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003ePower Consumption\u003c\/td\u003e\n\u003ctd\u003e0.3W-1.5W depending on workload\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003eCamera Interface\u003c\/td\u003e\n\u003ctd\u003eDVP parallel camera interface\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003eAudio Input\u003c\/td\u003e\n\u003ctd\u003eDual-channel PDM microphone input\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003eOperating Voltage\u003c\/td\u003e\n\u003ctd\u003e3.3V single supply\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003c\/table\u003e\n\n\u003ch2\u003eKey Features\u003c\/h2\u003e\n\u003cul\u003e\n\u003cli\u003eHardware-accelerated neural network inference with dedicated KPU co-processor delivering 0.3+ TFLOPS for INT8 quantized models, enabling real-time processing of complex CNN architectures\u003c\/li\u003e\n\u003cli\u003eIntegrated dual-channel audio processing with onboard microphone support for voice recognition, keyword spotting, and audio classification without external audio codecs\u003c\/li\u003e\n\u003cli\u003eDirect camera interface supporting OV2640 and OV5640 sensors for immediate image capture and processing, enabling real-time computer vision applications like face detection and object recognition\u003c\/li\u003e\n\u003cli\u003eExceptionally low power consumption at 0.3W during inference, making it suitable for battery-powered IoT devices, drones, and mobile robotics requiring extended runtime\u003c\/li\u003e\n\u003cli\u003eComprehensive peripheral ecosystem including SPI, I2C, UART, GPIO, and PWM for seamless integration with sensors, actuators, and external hardware\u003c\/li\u003e\n\u003cli\u003eOpen-source MicroPython and C\/C++ development environments with extensive documentation and community support for rapid prototyping and deployment\u003c\/li\u003e\n\u003c\/ul\u003e\n\n\u003ch2\u003eApplications and Use Cases\u003c\/h2\u003e\n\u003cul\u003e\n\u003cli\u003eIntelligent surveillance systems with onboard face detection and person counting using YOLOv2 models, eliminating the need for continuous cloud connectivity and reducing bandwidth costs\u003c\/li\u003e\n\u003cli\u003eAutonomous robotics platforms requiring real-time visual servoing, obstacle detection, and path planning with edge-based inference for responsive control without latency\u003c\/li\u003e\n\u003cli\u003eSmart IoT devices with voice command recognition and keyword spotting capabilities, enabling always-on listening with minimal power consumption for smart home applications\u003c\/li\u003e\n\u003cli\u003eIndustrial quality control systems performing real-time defect detection on production lines using custom CNN models trained for specific product characteristics and failure modes\u003c\/li\u003e\n\u003cli\u003eGesture recognition and pose estimation applications for human-computer interaction in AR\/VR systems and interactive installations requiring low-latency response\u003c\/li\u003e\n\u003cli\u003eEnvironmental monitoring stations with onboard bird species classification, insect detection, and acoustic analysis for biodiversity research and conservation applications\u003c\/li\u003e\n\u003c\/ul\u003e\n\n\u003ch2\u003eHow to Use\u003c\/h2\u003e\n\u003cp\u003eBegin by setting up your development environment using the Sipeed K210 SDK or the MicroPython firmware, available through the official Sipeed GitHub repository. Connect your K210 module via USB to your development machine, install the necessary drivers and flashing tools (such as kflash), and configure your IDE whether using VS Code with PlatformIO or the official Sipeed IDE. For camera-based applications, connect your OV2640 or OV5640 sensor to the DVP interface, ensuring proper pin alignment and secure connections to prevent signal integrity issues.\u003c\/p\u003e\n\n\u003cp\u003eModel deployment requires converting your trained neural networks to the K210-compatible format using the provided model conversion toolkit, which quantizes your models to INT8 precision and generates optimized binaries for the KPU accelerator. Load your converted model into the module's SRAM, initialize the camera or audio input peripherals as needed, and implement your inference loop using the KPU API functions. For optimal performance, profile your models using the provided benchmarking tools to understand latency characteristics, memory usage, and power consumption patterns. The module supports both synchronous inference blocking operations and asynchronous processing pipelines, allowing you to choose between simplicity and maximum throughput depending on your application requirements.\u003c\/p\u003e\n\n\u003ch2\u003eFrequently Asked Questions\u003c\/h2\u003e\n\u003cdetails\u003e\n\u003csummary\u003eWhat neural network models can run on the K210 module?\u003c\/summary\u003e\n\u003cp\u003eThe K210 supports quantized convolutional neural networks optimized for edge deployment, including MobileNet variants, YOLOv2 for object detection, and custom CNN architectures. Models must be converted to INT8 or INT16 quantized format using the Sipeed model conversion toolkit. The 8MB SRAM limitation means larger models like ResNet50 require model splitting or layer-by-layer processing. Typical inference times range from 10-100ms depending on model complexity and input resolution.\u003c\/p\u003e\n\u003c\/details\u003e\n\u003cdetails\u003e\n\u003csummary\u003eHow do I connect and configure a camera sensor with the K210?\u003c\/summary\u003e\n\u003cp\u003eThe K210 features a DVP parallel camera interface supporting OV2640 and OV5640 sensors. Connect the camera sensor to the designated DVP pins following the pinout diagram in the datasheet, ensuring proper voltage levels and signal integrity. Initialize the camera using the DVP driver functions in the SDK, configure resolution and frame rate parameters, and implement frame capture callbacks to process images in real-time. The module can capture VGA resolution frames at 30fps and QVGA at higher frame rates depending on your processing pipeline.\u003c\/p\u003e\n\u003c\/details\u003e\n\u003cdetails\u003e\n\u003csummary\u003eWhat is the maximum inference latency for real-time applications?\u003c\/summary\u003e\n\u003cp\u003eLatency depends on model complexity and input resolution. Simple object detection models like tiny YOLOv2 achieve 50-80ms per frame at QVGA resolution, while face detection models run at 30-50ms. The dual-core processor and KPU accelerator work in parallel, allowing preprocessing on one core while the KPU executes neural network operations. For time-critical robotics applications, latencies under 100ms are typical, making the K210 suitable for real-time control loops.\u003c\/p\u003e\n\u003c\/details\u003e\n\u003cdetails\u003e\n\u003csummary\u003eCan I use floating-point neural networks on the K210?\u003c\/summary\u003e\n\u003cp\u003eThe K210 hardware accelerator is optimized exclusively for quantized INT8 and INT16 operations. Floating-point models must be quantized before deployment using post-training quantization or quantization-aware training techniques. The Sipeed conversion toolkit automates this process, typically achieving 95-99% accuracy retention compared to original floating-point models. If your application requires floating-point precision, you can run inference on the RISC-V cores without KPU acceleration, but performance will be significantly reduced.\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 M1W AI+lOT Module K210 Deep learning Online in India\u003c\/h2\u003e\n\u003cp\u003ePurchase the \u003cstrong\u003eM1W AI+lOT Module K210 Deep learning\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\u003eM1W AI+lOT Module K210 Deep learning\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":43856803627171,"sku":"TES-EV00082067","price":1882.56,"currency_code":"INR","in_stock":true}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/0628\/4479\/7091\/products\/1_DFR0638-228x228.jpg?v=1704280769","url":"https:\/\/www.theengineerstore.in\/zh-hans\/products\/m1w-ai-lot-module-k210-deep-learning","provider":"The Engineer Store","version":"1.0","type":"link"}