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AI-deck 1.1
The AI-deck 1.1 is a compact AI acceleration module designed for embedded machine learning applications, featuring onboard neural processing capabilities for real-time inference on edge devices. Professional developers, roboticists, and embedded systems engineers use this platform to deploy computer vision and AI models directly on resource-constrained hardware without cloud dependency. It solves the critical challenge of running sophisticated AI algorithms locally with minimal latency, power consumption, and bandwidth requirements.
Product Overview
The AI-deck 1.1 integrates a specialized neural processing unit with a dual-core Xtensa processor, enabling efficient execution of TensorFlow Lite and ONNX models at the edge. This architecture eliminates the need for continuous cloud connectivity, making it ideal for autonomous systems, industrial IoT, and time-critical applications where millisecond-level latency is essential. The module operates on ultra-low power consumption, typically under 1W during active inference, making it suitable for battery-powered and energy-harvesting applications.
What distinguishes the AI-deck 1.1 is its integrated memory hierarchy with 2.6MB of SRAM and support for external flash storage, combined with optimized quantization support for INT8 and INT16 models. The platform includes hardware acceleration for common neural network operations including convolution, pooling, and fully connected layers, delivering up to 100 GOPS of peak performance. Its modular design allows seamless integration with microcontroller platforms and development boards, making it accessible for prototyping and production deployment.
Key Specifications
| Specification | Details |
| Product Type | AI Acceleration Module for Edge Computing |
| 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 |
| Neural Processing Unit | Specialized NPU with 100 GOPS peak performance |
| Processor | Dual-core Xtensa LX7 at 160 MHz |
| Memory | 2.6MB SRAM, External Flash Support |
| Power Consumption | Less than 1W during active inference |
| Supported Frameworks | TensorFlow Lite, ONNX, Keras |
| Model Quantization | INT8 and INT16 support for optimized inference |
Key Features
- Dedicated Neural Processing Unit delivering 100 GOPS of computational throughput for accelerated machine learning inference on edge devices
- Ultra-low power architecture consuming less than 1W during active operation, enabling deployment in battery-powered and IoT applications
- Integrated 2.6MB SRAM with external flash storage support for flexible model deployment and data buffering
- Hardware acceleration for standard neural network operations including convolution, pooling, activation functions, and fully connected layers
- Support for quantized models in INT8 and INT16 formats, reducing model size by up to 75% while maintaining accuracy
- Dual-core Xtensa processor running at 160 MHz for flexible preprocessing and postprocessing tasks
- Compatible with popular ML frameworks including TensorFlow Lite, ONNX, and Keras for streamlined development workflow
- Modular design with standardized interfaces for integration with microcontroller platforms and development boards
Applications and Use Cases
- Autonomous robotics and drone navigation using real-time object detection and pose estimation models without relying on external servers
- Industrial predictive maintenance systems analyzing vibration and thermal data through edge-deployed neural networks for fault detection
- Smart surveillance and security systems running face recognition and anomaly detection algorithms locally for privacy-preserving monitoring
- Wearable health monitoring devices executing ECG analysis, activity recognition, and fall detection models with minimal power draw
- Agricultural IoT applications performing crop disease detection and pest identification through computer vision at field deployment sites
- Smart city infrastructure monitoring with edge-deployed models for traffic flow analysis and environmental sensor data processing
How to Use
Begin by connecting the AI-deck 1.1 to your development board or microcontroller platform using the provided connector interface. Install the necessary toolchain including the Espressif IDF and TensorFlow Lite Micro libraries on your development machine. Convert your pre-trained neural network model to TensorFlow Lite format and apply quantization techniques to optimize for the target hardware, ensuring your model fits within the 2.6MB SRAM constraint.
Load your quantized model onto the AI-deck 1.1 using the flash programming interface and implement the inference pipeline through the provided C/C++ API. Configure the neural processing unit parameters including batch size, input/output tensor dimensions, and memory allocation strategies. Test your deployment with representative input data, monitor inference latency and power consumption metrics, and iterate on model optimization using the built-in profiling tools. For production deployment, implement error handling, model versioning, and over-the-air update mechanisms to ensure reliability and maintainability.
Frequently Asked Questions
What machine learning models can run on the AI-deck 1.1?
The AI-deck 1.1 supports any TensorFlow Lite or ONNX model that can be quantized to INT8 or INT16 format and compressed to fit within the 2.6MB SRAM. Common deployable models include MobileNet for image classification, SSD for object detection, and LSTM networks for time-series analysis. Model size and complexity must be optimized through quantization, pruning, and knowledge distillation techniques to achieve acceptable inference latency on the target hardware.
What is the typical inference latency for computer vision models?
Inference latency depends on model complexity and input resolution. For optimized MobileNet v2 on 224x224 images, expect 50-100ms latency. Smaller models or lower resolution inputs achieve sub-50ms performance. The dual-core processor handles preprocessing while the NPU executes the neural network, enabling pipeline parallelism. Actual latency should be measured on your specific model and input data using the integrated profiling tools.
Can the AI-deck 1.1 be used with microcontroller platforms other than Crazyflie?
Yes, the AI-deck 1.1 features a standardized SPI interface compatible with various microcontroller platforms. However, integration requires custom firmware development to handle communication protocols, memory management, and model loading. The reference implementation and documentation support integration with ARM Cortex-M based microcontrollers and other platforms with adequate SPI bandwidth and GPIO availability.
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 AI-deck 1.1 Online in India
Purchase the AI-deck 1.1 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 AI-deck 1.1 with fast shipping and expert support.
Our team in Bengaluru is available 24/7 to support your journey from product selection to project completion.
AI-deck 1.1
The AI-deck 1.1 is a compact AI acceleration module designed for embedded machine learning applications, featuring onboard neural processing capabilities for real-time inference on edge devices. Professional developers, roboticists, and embedded systems engineers use this platform to deploy computer vision and AI models directly on resource-constrained hardware without cloud dependency. It solves the critical challenge of running sophisticated AI algorithms locally with minimal latency, power consumption, and bandwidth requirements.
Product Overview
The AI-deck 1.1 integrates a specialized neural processing unit with a dual-core Xtensa processor, enabling efficient execution of TensorFlow Lite and ONNX models at the edge. This architecture eliminates the need for continuous cloud connectivity, making it ideal for autonomous systems, industrial IoT, and time-critical applications where millisecond-level latency is essential. The module operates on ultra-low power consumption, typically under 1W during active inference, making it suitable for battery-powered and energy-harvesting applications.
What distinguishes the AI-deck 1.1 is its integrated memory hierarchy with 2.6MB of SRAM and support for external flash storage, combined with optimized quantization support for INT8 and INT16 models. The platform includes hardware acceleration for common neural network operations including convolution, pooling, and fully connected layers, delivering up to 100 GOPS of peak performance. Its modular design allows seamless integration with microcontroller platforms and development boards, making it accessible for prototyping and production deployment.
Key Specifications
| Specification | Details |
| Product Type | AI Acceleration Module for Edge Computing |
| 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 |
| Neural Processing Unit | Specialized NPU with 100 GOPS peak performance |
| Processor | Dual-core Xtensa LX7 at 160 MHz |
| Memory | 2.6MB SRAM, External Flash Support |
| Power Consumption | Less than 1W during active inference |
| Supported Frameworks | TensorFlow Lite, ONNX, Keras |
| Model Quantization | INT8 and INT16 support for optimized inference |
Key Features
- Dedicated Neural Processing Unit delivering 100 GOPS of computational throughput for accelerated machine learning inference on edge devices
- Ultra-low power architecture consuming less than 1W during active operation, enabling deployment in battery-powered and IoT applications
- Integrated 2.6MB SRAM with external flash storage support for flexible model deployment and data buffering
- Hardware acceleration for standard neural network operations including convolution, pooling, activation functions, and fully connected layers
- Support for quantized models in INT8 and INT16 formats, reducing model size by up to 75% while maintaining accuracy
- Dual-core Xtensa processor running at 160 MHz for flexible preprocessing and postprocessing tasks
- Compatible with popular ML frameworks including TensorFlow Lite, ONNX, and Keras for streamlined development workflow
- Modular design with standardized interfaces for integration with microcontroller platforms and development boards
Applications and Use Cases
- Autonomous robotics and drone navigation using real-time object detection and pose estimation models without relying on external servers
- Industrial predictive maintenance systems analyzing vibration and thermal data through edge-deployed neural networks for fault detection
- Smart surveillance and security systems running face recognition and anomaly detection algorithms locally for privacy-preserving monitoring
- Wearable health monitoring devices executing ECG analysis, activity recognition, and fall detection models with minimal power draw
- Agricultural IoT applications performing crop disease detection and pest identification through computer vision at field deployment sites
- Smart city infrastructure monitoring with edge-deployed models for traffic flow analysis and environmental sensor data processing
How to Use
Begin by connecting the AI-deck 1.1 to your development board or microcontroller platform using the provided connector interface. Install the necessary toolchain including the Espressif IDF and TensorFlow Lite Micro libraries on your development machine. Convert your pre-trained neural network model to TensorFlow Lite format and apply quantization techniques to optimize for the target hardware, ensuring your model fits within the 2.6MB SRAM constraint.
Load your quantized model onto the AI-deck 1.1 using the flash programming interface and implement the inference pipeline through the provided C/C++ API. Configure the neural processing unit parameters including batch size, input/output tensor dimensions, and memory allocation strategies. Test your deployment with representative input data, monitor inference latency and power consumption metrics, and iterate on model optimization using the built-in profiling tools. For production deployment, implement error handling, model versioning, and over-the-air update mechanisms to ensure reliability and maintainability.
Frequently Asked Questions
What machine learning models can run on the AI-deck 1.1?
The AI-deck 1.1 supports any TensorFlow Lite or ONNX model that can be quantized to INT8 or INT16 format and compressed to fit within the 2.6MB SRAM. Common deployable models include MobileNet for image classification, SSD for object detection, and LSTM networks for time-series analysis. Model size and complexity must be optimized through quantization, pruning, and knowledge distillation techniques to achieve acceptable inference latency on the target hardware.
What is the typical inference latency for computer vision models?
Inference latency depends on model complexity and input resolution. For optimized MobileNet v2 on 224x224 images, expect 50-100ms latency. Smaller models or lower resolution inputs achieve sub-50ms performance. The dual-core processor handles preprocessing while the NPU executes the neural network, enabling pipeline parallelism. Actual latency should be measured on your specific model and input data using the integrated profiling tools.
Can the AI-deck 1.1 be used with microcontroller platforms other than Crazyflie?
Yes, the AI-deck 1.1 features a standardized SPI interface compatible with various microcontroller platforms. However, integration requires custom firmware development to handle communication protocols, memory management, and model loading. The reference implementation and documentation support integration with ARM Cortex-M based microcontrollers and other platforms with adequate SPI bandwidth and GPIO availability.
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 AI-deck 1.1 Online in India
Purchase the AI-deck 1.1 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 AI-deck 1.1 with fast shipping and expert support.
Our team in Bengaluru is available 24/7 to support your journey from product selection to project completion.