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Sipeed MAIX-I module
The 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.
Product Overview
The 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.
What 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.
Key Specifications
| Specification | Details |
| Product Type | AI Accelerator Module with Embedded Neural Network Processor |
| Brand | Sipeed |
| Origin | Original/Authentic |
| Warranty | 7 days on manufacturing defects |
| Shipping | 1-5 days from Bengaluru |
| Delivery | 7-8 days across India |
| Support | 24/7 via Email and WhatsApp |
| Processor | Kendryte K210 Dual-Core RISC-V at 400MHz |
| Neural Network Accelerator | 1 TOPS Peak Performance, INT8 Quantization Support |
| On-Chip Memory | 8MB SRAM, 2MB ROM |
| Power Consumption | 0.3W Active Mode, 50mW Sleep Mode |
| Operating Voltage | 3.3V to 5V with integrated voltage regulator |
| Interfaces | UART, SPI, I2C, GPIO, DVP Camera Interface |
Key Features
- Dedicated Neural Network Accelerator delivering 1 TOPS peak throughput for real-time CNN inference without CPU bottlenecks
- Dual-core RISC-V processor architecture enabling parallel execution of AI models and control logic simultaneously
- 8MB on-chip SRAM with configurable cache hierarchy optimized for convolutional operations and feature map storage
- Integrated DVP camera interface supporting OV2640 and OV7740 sensors for direct image capture and preprocessing
- Ultra-low power consumption enabling deployment in battery-powered IoT and edge devices with extended runtime
- Support for MobileNet, SqueezeNet, and custom quantized models through nncase compiler toolchain
- Hardware-accelerated image preprocessing including scaling, rotation, and color space conversion
- Flexible memory configuration with external PSRAM support up to 32MB for larger model deployment
Applications and Use Cases
- Face Detection and Recognition in smart surveillance systems and access control terminals requiring local processing without cloud dependency
- Object Detection and Tracking in autonomous robotics and industrial inspection systems for real-time visual feedback
- Gesture Recognition in human-computer interaction applications and smart home control interfaces
- License Plate Recognition and Vehicle Classification in traffic monitoring and parking management systems
- Anomaly Detection in manufacturing quality control where edge processing reduces latency and bandwidth requirements
- Pose Estimation in fitness tracking and ergonomic monitoring applications for wearable devices
How to Use
Begin 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.
For 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.
Frequently Asked Questions
What is the maximum resolution and frame rate for camera input on the MAIX-I module?
The 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.
Can I run multiple neural network models sequentially or in parallel on the MAIX-I?
The 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.
What neural network model sizes and architectures are compatible with the K210 accelerator?
The 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.
When will I receive my order?
Orders are dispatched within 1-5 business days from our Bengaluru warehouse. Delivery takes 7-8 days to most locations across India.
What is your return and warranty policy?
We 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.
Are bulk discounts available?
Yes, wholesale pricing for orders of 10 or more units. Contact our sales team via WhatsApp or email for a customized bulk quote.
Why Buy from The Engineer Store
- Genuine Products: Sourced directly from authorized distributors with authentication
- Expert Team: Our technical team validates every product before listing
- Fast Shipping: Dispatched within 1-5 days from our Bengaluru warehouse
- Pan-India Delivery: 7-8 days to Mumbai, Delhi, Chennai, Hyderabad, Pune, Kolkata
- Payment Options: COD, UPI, credit/debit cards, net banking, EMI available
- Technical Support: 24/7 expert guidance via email and WhatsApp
- Returns: 7-day return policy on manufacturing defects only
Buy Sipeed MAIX-I module Online in India
Purchase the Sipeed MAIX-I module online at The Engineer Store, 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 Sipeed MAIX-I module with fast shipping and expert support.
Our team in Bengaluru is available 24/7 to support your journey from product selection to project completion.
Sipeed MAIX-I module
The 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.
Product Overview
The 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.
What 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.
Key Specifications
| Specification | Details |
| Product Type | AI Accelerator Module with Embedded Neural Network Processor |
| Brand | Sipeed |
| Origin | Original/Authentic |
| Warranty | 7 days on manufacturing defects |
| Shipping | 1-5 days from Bengaluru |
| Delivery | 7-8 days across India |
| Support | 24/7 via Email and WhatsApp |
| Processor | Kendryte K210 Dual-Core RISC-V at 400MHz |
| Neural Network Accelerator | 1 TOPS Peak Performance, INT8 Quantization Support |
| On-Chip Memory | 8MB SRAM, 2MB ROM |
| Power Consumption | 0.3W Active Mode, 50mW Sleep Mode |
| Operating Voltage | 3.3V to 5V with integrated voltage regulator |
| Interfaces | UART, SPI, I2C, GPIO, DVP Camera Interface |
Key Features
- Dedicated Neural Network Accelerator delivering 1 TOPS peak throughput for real-time CNN inference without CPU bottlenecks
- Dual-core RISC-V processor architecture enabling parallel execution of AI models and control logic simultaneously
- 8MB on-chip SRAM with configurable cache hierarchy optimized for convolutional operations and feature map storage
- Integrated DVP camera interface supporting OV2640 and OV7740 sensors for direct image capture and preprocessing
- Ultra-low power consumption enabling deployment in battery-powered IoT and edge devices with extended runtime
- Support for MobileNet, SqueezeNet, and custom quantized models through nncase compiler toolchain
- Hardware-accelerated image preprocessing including scaling, rotation, and color space conversion
- Flexible memory configuration with external PSRAM support up to 32MB for larger model deployment
Applications and Use Cases
- Face Detection and Recognition in smart surveillance systems and access control terminals requiring local processing without cloud dependency
- Object Detection and Tracking in autonomous robotics and industrial inspection systems for real-time visual feedback
- Gesture Recognition in human-computer interaction applications and smart home control interfaces
- License Plate Recognition and Vehicle Classification in traffic monitoring and parking management systems
- Anomaly Detection in manufacturing quality control where edge processing reduces latency and bandwidth requirements
- Pose Estimation in fitness tracking and ergonomic monitoring applications for wearable devices
How to Use
Begin 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.
For 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.
Frequently Asked Questions
What is the maximum resolution and frame rate for camera input on the MAIX-I module?
The 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.
Can I run multiple neural network models sequentially or in parallel on the MAIX-I?
The 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.
What neural network model sizes and architectures are compatible with the K210 accelerator?
The 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.
When will I receive my order?
Orders are dispatched within 1-5 business days from our Bengaluru warehouse. Delivery takes 7-8 days to most locations across India.
What is your return and warranty policy?
We 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.
Are bulk discounts available?
Yes, wholesale pricing for orders of 10 or more units. Contact our sales team via WhatsApp or email for a customized bulk quote.
Why Buy from The Engineer Store
- Genuine Products: Sourced directly from authorized distributors with authentication
- Expert Team: Our technical team validates every product before listing
- Fast Shipping: Dispatched within 1-5 days from our Bengaluru warehouse
- Pan-India Delivery: 7-8 days to Mumbai, Delhi, Chennai, Hyderabad, Pune, Kolkata
- Payment Options: COD, UPI, credit/debit cards, net banking, EMI available
- Technical Support: 24/7 expert guidance via email and WhatsApp
- Returns: 7-day return policy on manufacturing defects only
Buy Sipeed MAIX-I module Online in India
Purchase the Sipeed MAIX-I module online at The Engineer Store, 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 Sipeed MAIX-I module with fast shipping and expert support.
Our team in Bengaluru is available 24/7 to support your journey from product selection to project completion.