Sipeed MAIX-I module w/o WiFi ( 1st RISC-V 64 AI Module, K210 inside )
Sipeed MAIX-I module w/o WiFi ( 1st RISC-V 64 AI Module, K210 inside )
Sipeed MAIX-I module w/o WiFi ( 1st RISC-V 64 AI Module, K210 inside )
The Sipeed MAIX-I is the first commercial RISC-V 64-bit AI accelerator module powered by the Kendryte K210 dual-core processor, designed for edge AI inference and machine learning applications without WiFi connectivity. This module is extensively used by embedded systems engineers, robotics developers, and IoT specialists who require low-power AI processing at the edge without network dependencies. It solves the critical challenge of deploying neural networks and computer vision algorithms on resource-constrained devices while maintaining ultra-low power consumption and deterministic latency.
Product Overview
The Sipeed MAIX-I module integrates the K210 System-on-Chip featuring dual-core RISC-V processors running at 400MHz, with an integrated neural network accelerator (KPU) capable of processing convolutional neural networks with 8-bit quantized models. The architecture includes 8MB of SRAM for fast computation, hardware support for common activation functions, and dedicated image processing units for real-time computer vision tasks. The module operates at exceptionally low power consumption (typically under 1W during inference), making it ideal for battery-powered and embedded applications where traditional GPU-based solutions are impractical.
What distinguishes the MAIX-I is its purpose-built neural processing architecture optimized for inference rather than training, combined with the RISC-V instruction set that provides open-source software ecosystem advantages. The absence of WiFi in this variant reduces power consumption and cost while maintaining full AI processing capabilities, making it perfect for standalone edge devices, autonomous robots, industrial vision systems, and smart sensors. The module supports popular frameworks like TensorFlow Lite and ONNX through conversion tools, enabling developers to deploy pre-trained models with minimal optimization effort.
Key Specifications
| Specification | Details |
| Product Type | RISC-V 64-bit AI Accelerator Module |
| Brand | Sipeed |
| Processor | Kendryte K210 Dual-Core RISC-V 64-bit at 400MHz |
| Neural Network Accelerator | KPU with 8-bit quantization support |
| Memory | 8MB SRAM, supports external SPI Flash |
| Power Consumption | Typically under 1W during inference |
| Connectivity | No WiFi (WiFi-less variant) |
| Interfaces | SPI, I2C, UART, GPIO |
| Operating Voltage | 1.8V to 3.3V |
| 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 |
Key Features
- Dual-Core RISC-V 64-bit Processor at 400MHz with dedicated KPU neural network accelerator for real-time AI inference without external GPU dependency
- 8MB embedded SRAM enabling fast model execution with support for 8-bit quantized neural networks and common activation functions
- Ultra-low power consumption under 1W during inference, ideal for battery-powered IoT devices, drones, and mobile robotics applications
- Open RISC-V instruction set architecture with comprehensive software ecosystem including MicroPython, Rust, and C/C++ support
- Integrated image processing unit supporting camera input for real-time computer vision tasks including object detection and face recognition
- Multiple communication interfaces including SPI, I2C, UART, and GPIO for seamless integration with sensors and peripherals
Applications and Use Cases
- Edge AI Computer Vision: Deploy real-time object detection, face recognition, and pose estimation on autonomous robots and surveillance systems without cloud connectivity
- Industrial IoT and Smart Sensors: Implement predictive maintenance models and anomaly detection on factory equipment with deterministic latency and offline processing capability
- Autonomous Robotics: Power self-driving robots, drones, and autonomous vehicles with onboard AI inference for navigation, obstacle avoidance, and decision-making
- Smart Home and Consumer Electronics: Enable intelligent features in resource-constrained devices like smart doorbells, security cameras, and wearable devices with privacy-preserving local processing
- Medical and Healthcare Devices: Deploy diagnostic algorithms and vital sign monitoring on portable medical devices requiring real-time analysis without network latency
- Agricultural Technology: Implement crop disease detection, pest monitoring, and yield prediction models on field robots and drones for precision farming applications
How to Use
To begin working with the Sipeed MAIX-I module, first establish the development environment by installing the Kendryte toolchain and MaixPy IDE on your computer. Connect the module to your development machine via USB, then use the IDE to write Python or C code for your AI application. The module supports importing pre-trained neural network models in ONNX or TensorFlow format, which can be quantized to 8-bit using provided conversion tools to fit within the 8MB SRAM constraint while maintaining acceptable accuracy.
For hardware integration, connect your camera module to the DVP camera interface, and configure other sensors through the available SPI, I2C, or UART interfaces using GPIO pins. The module operates at 1.8V to 3.3V, making it compatible with standard 3.3V logic circuits and microcontroller ecosystems. Program the inference pipeline to capture frames, preprocess images, run KPU inference, and output results through your chosen interface. The open-source MaixPy framework provides extensive examples for common tasks like object detection and face recognition, significantly accelerating development time for embedded AI projects.
Frequently Asked Questions
What is the difference between the MAIX-I with and without WiFi?
The WiFi-less variant (this product) eliminates the WiFi module, reducing power consumption, cost, and PCB footprint while maintaining full AI processing capabilities. Choose this version if your application requires standalone edge inference without cloud connectivity. The WiFi variant is suitable for applications needing remote monitoring or firmware updates over the network.
Can I run TensorFlow or PyTorch models directly on the MAIX-I?
The MAIX-I requires model conversion and quantization before deployment. TensorFlow Lite and ONNX models can be converted to the K210 format using provided tools. PyTorch models must first be converted to ONNX format, then to K210 format. The conversion process includes 8-bit quantization to fit within memory constraints. We recommend using pre-trained quantized models from the Sipeed model zoo for fastest deployment.
What is the maximum inference speed and accuracy for neural networks?
Inference speed depends on model architecture and complexity, typically ranging from 10-100 FPS for common object detection models. The KPU supports 8-bit quantized models with minimal accuracy loss compared to full-precision versions. For a 224x224 MobileNet-style model, expect approximately 30-50 FPS. Larger models may require optimization or model pruning to achieve real-time performance within the 8MB SRAM limit.
Is external storage available for larger models?
Yes, the K210 supports external SPI Flash for storing multiple models. While only 8MB SRAM is available for active inference, you can load different models from external storage as needed. This enables applications with multiple AI models that are loaded sequentially based on runtime requirements.
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.
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- Genuine Products: Sourced directly from authorized distributors with authentication
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- 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
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