Crazyflie AI-deck – with GAP8 RISC-V MCU and ESP32 Wi-Fi

The Crazyflie AI-deck is an advanced expansion module for the Crazyflie nano quadcopter platform, featuring the GAP8 RISC-V MCU for parallel processing and ESP32 Wi-Fi connectivity for wireless control and data streaming. Roboticists, drone researchers, and embedded systems engineers use this deck to deploy machine learning models, computer vision algorithms, and real-time autonomous flight control directly on the drone without external computational overhead. This product solves the critical challenge of running AI inference on resource-constrained aerial platforms while maintaining flight stability and reducing latency in edge computing applications.

Product Overview

The Crazyflie AI-deck integrates the GreenWaves GAP8 RISC-V microcontroller, a parallel processing unit specifically optimized for machine learning inference and signal processing on embedded devices. The GAP8 MCU features 8 parallel cores capable of executing convolutional neural networks, image classification, and sensor fusion algorithms at ultra-low power consumption. The deck also incorporates the ESP32 Wi-Fi module, enabling wireless connectivity for remote telemetry, firmware updates, and real-time video streaming from onboard cameras. This combination allows researchers to prototype and deploy sophisticated AI applications on a flying platform weighing just grams, eliminating the need for external computing infrastructure or tethered connections.

The architecture is purpose-built for edge AI deployment in aerial robotics. The GAP8 processor operates at frequencies up to 200 MHz with dedicated hardware accelerators for neural network operations, achieving inference speeds comparable to desktop GPUs while consuming less than 1W of power. The ESP32 provides dual-band Wi-Fi (2.4 GHz and 5 GHz) with integrated Bluetooth for multi-protocol communication. The deck connects seamlessly to the Crazyflie 2.1 platform via the expansion connector, inheriting the drone's gyroscope, accelerometer, and barometer data while adding computational intelligence for autonomous decision-making. This modular approach enables rapid iteration in research environments without redesigning the entire drone platform.

Key Specifications

Specification	Details
Product Type	AI Expansion Deck for Crazyflie Nano Quadcopter
Brand	Bitcraze
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
Primary Processor	GreenWaves GAP8 RISC-V MCU with 8 Parallel Cores
Processor Frequency	Up to 200 MHz
Memory	512 KB L1 SRAM, 8 MB L2 Flash
Wi-Fi Module	ESP32 Dual-Band (2.4 GHz and 5 GHz)
Power Consumption	Less than 1W during inference
Weight	Approximately 2.5 grams
Compatibility	Crazyflie 2.1 and later versions
Neural Network Support	TensorFlow Lite, ONNX, Caffe models

Key Features

• GAP8 RISC-V Parallel Processing: 8-core architecture with dedicated hardware accelerators for CNN inference, enabling real-time machine learning on the drone without external computational dependencies
• Ultra-Low Power AI Inference: Consumes less than 1W during neural network operations, extending flight time while running complex AI algorithms simultaneously with flight control
• ESP32 Wi-Fi Connectivity: Dual-band wireless module for remote telemetry streaming, model updates, and real-time video transmission at up to 150 Mbps throughput
• Seamless Integration: Connects to Crazyflie 2.1 via standard expansion connector with automatic sensor fusion of IMU, barometer, and onboard camera data
• Framework Support: Compatible with TensorFlow Lite, ONNX, and Caffe models, allowing researchers to deploy pre-trained networks with minimal optimization
• Development Ecosystem: Includes Python SDK, C/C++ libraries, and extensive documentation for rapid prototyping and custom algorithm development

Applications and Use Cases

• Autonomous Drone Navigation: Deploy obstacle detection and avoidance algorithms using onboard camera input processed by the GAP8 MCU, enabling fully autonomous flight in GPS-denied environments without relying on external servers
• Real-Time Object Detection: Run YOLO or MobileNet models for target tracking, person detection, and environmental mapping with sub-100ms latency, critical for search-and-rescue and surveillance applications
• Gesture Recognition and Pose Estimation: Process camera frames for human gesture recognition or pose estimation in human-robot interaction research, enabling drones to respond to hand signals and body movements
• Swarm Robotics and Multi-Agent Coordination: Use Wi-Fi connectivity and onboard processing for decentralized swarm control algorithms, allowing multiple Crazyflie drones to collaborate without centralized server infrastructure
• Aerial Environmental Monitoring: Deploy sensor fusion algorithms combining accelerometer, gyroscope, and barometer data with machine learning models for anomaly detection in industrial inspection and environmental monitoring
• Edge AI Research: Prototype and validate novel neural network compression techniques, quantization strategies, and embedded ML algorithms in a real-world aerial platform with immediate hardware feedback

How to Use

Begin by mounting the Crazyflie AI-deck onto your Crazyflie 2.1 platform using the expansion connector, ensuring all pins are properly aligned and the deck sits flush against the drone frame. Power on the Crazyflie and verify that the GAP8 and ESP32 modules are detected by the firmware using the Crazyflie client software. Download and install the Crazyflie Python SDK on your development machine, then use the provided examples to establish Wi-Fi communication with the drone. The AI-deck firmware includes pre-built support for TensorFlow Lite models, allowing you to convert your trained neural networks to the appropriate format using the provided quantization tools and deploy them directly to the deck's 8MB flash memory.

For custom applications, develop your inference code in C or Python using the GAP8 SDK, leveraging the 8 parallel cores for accelerated computation. Use the onboard camera interface to stream image data to the GAP8 processor, process the frames through your neural network, and transmit results via ESP32 Wi-Fi for real-time monitoring or cloud integration. The modular architecture allows you to test algorithms incrementally, starting with simple classification tasks and progressing to complex multi-model pipelines. Monitor power consumption and inference latency using the built-in profiling tools to optimize your models for the constrained aerial environment. Join the active Bitcraze community forums to access pre-trained model repositories, optimization techniques, and troubleshooting guidance from experienced researchers worldwide.

Frequently Asked Questions

Can I run multiple neural networks simultaneously on the GAP8 MCU?

Yes, the GAP8's 8-core architecture and 512KB L1 SRAM support running multiple models in sequence or with careful memory management, in parallel. However, you must consider the total memory footprint and computational budget. For example, you can run a lightweight object detection model followed by a pose estimation model within a single control loop, provided the combined inference time stays below your flight control update frequency (typically 100-200 Hz). Use the GAP8 profiling tools to measure execution time and memory usage for each model component.

What is the maximum resolution and frame rate for camera input processing?

The AI-deck can process camera frames up to 320x240 pixels at 30 FPS when running typical CNN models. For higher resolutions like 640x480, you may need to reduce frame rate or use model quantization and pruning techniques to stay within the computational budget. The actual performance depends on your specific neural network architecture, model size, and the number of parallel cores allocated to image processing versus other tasks.

How do I convert my TensorFlow or PyTorch model for deployment on the GAP8?

Use the GreenWaves AutoTiler framework and quantization tools to convert your models. First, export your trained model from TensorFlow or PyTorch to ONNX or TensorFlow Lite format. Then use the AutoTiler to generate optimized C code that exploits the GAP8's parallel architecture. The framework automatically handles memory allocation, core distribution, and performance optimization. Bitcraze provides detailed tutorials and example scripts in the AI-deck documentation to guide you through this conversion pipeline.

What is the Wi-Fi range and data throughput of the ESP32 module?

The ESP32 provides a typical Wi-Fi range of 100-150 meters in open environments with line-of-sight, and 30-50 meters indoors depending on obstacles and interference. Data throughput reaches up to 150 Mbps on 5 GHz and 72 Mbps on 2.4 GHz. For drone telemetry and video streaming, the actual bandwidth required is much lower, typically 1-5 Mbps for real-time video at reduced resolution, allowing reliable operation at extended ranges.

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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• Returns: 7-day return policy on manufacturing defects only

Buy Crazyflie AI-deck – with GAP8 RI

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