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TinyML & Edge AI Features

Overview

Accelerapp now includes comprehensive TinyML (Tiny Machine Learning) and Edge AI capabilities, enabling developers to generate optimized machine learning code for microcontrollers and embedded systems.

Key Features

🧠 On-Device Inference

Generate optimized neural network inference code for resource-constrained devices:

  • Automatic code generation for Arduino, ESP32, STM32, and more
  • Optimized for low memory footprint (32-128KB tensor arena)
  • Supports int8 quantization for 4x size reduction
  • Performance estimates for each platform

🔧 Model Optimization

Convert and optimize trained models for edge deployment:

  • Quantization (int8, reducing model size by ~75%)
  • Pruning (removing unnecessary weights)
  • Knowledge distillation
  • Weight sharing
  • TensorFlow Lite Micro compatible output

🔒 Federated Learning

Privacy-preserving distributed learning infrastructure:

  • Local training on device data
  • Gradient aggregation without sharing raw data
  • Differential privacy support
  • Secure communication protocols
  • Suitable for privacy-sensitive applications

🎯 Adaptive Behavior

Enable devices to learn and adapt in real-time:

  • Online learning capabilities
  • Environment adaptation
  • Behavior optimization based on sensor data
  • Resource-aware execution
  • Perfect for smart IoT devices

Supported Platforms

Platform Devices RAM Flash Status
Arduino Nano 33 BLE, Portenta H7, Nano RP2040 256KB - 8MB 1MB - 16MB ✅ Full Support
ESP32 ESP32, ESP32-S3 520KB 4MB - 16MB ✅ Full Support
STM32 F4, F7, H7 series 192KB - 1MB 1MB - 2MB ✅ Full Support
Nordic nRF52840, nRF5340 256KB - 1MB 1MB - 2MB ✅ Full Support
Raspberry Pi Pico RP2040 264KB 2MB ✅ Full Support

Quick Start

Installation

pip install accelerapp

Basic Usage

from accelerapp.agents import TinyMLAgent

# Initialize the agent
agent = TinyMLAgent()

# Generate inference code
spec = {
    "task_type": "inference",
    "platform": "arduino",
    "model_type": "classification",
    "input_shape": [1, 96, 96, 1],  # 96x96 grayscale image
    "num_classes": 5
}

result = agent.generate(spec)

# Save generated files
with open("ml_inference.h", "w") as f:
    f.write(result["files"]["ml_inference.h"])
    
with open("ml_inference.c", "w") as f:
    f.write(result["files"]["ml_inference.c"])

print(f"✓ Memory required: {result['memory_estimate']['total_estimated']}")
print(f"✓ Inference time: {result['performance_estimate']['inference_time']}")

Integration with Firmware Generator

from accelerapp.firmware.generator import FirmwareGenerator
from pathlib import Path

# Hardware specification with ML
hardware_spec = {
    "platform": "arduino",
    "device_name": "SmartCamera",
    "peripherals": [
        {"type": "camera", "interface": "i2c"}
    ],
    "ml_config": {
        "task_type": "inference",
        "model_type": "classification",
        "num_classes": 3
    }
}

# Generate complete firmware with ML
generator = FirmwareGenerator(hardware_spec)
result = generator.generate(Path("./output"))

print(f"✓ Generated {len(result['files_generated'])} files")
print(f"✓ ML enabled: {result['ml_enabled']}")

Use Cases

1. Smart Home Devices

  • Voice command recognition
  • Gesture control
  • Occupancy detection
  • Energy usage prediction

2. Industrial IoT

  • Predictive maintenance
  • Anomaly detection
  • Quality control
  • Process optimization

3. Healthcare Devices

  • Vital signs monitoring
  • Fall detection
  • Sleep pattern analysis
  • ECG classification

4. Agricultural IoT

  • Crop disease detection
  • Soil moisture prediction
  • Pest identification
  • Weather forecasting

5. Environmental Monitoring

  • Air quality sensing
  • Water quality analysis
  • Wildlife tracking
  • Climate monitoring

Performance Benchmarks

Inference Time (Classification Model)

Platform Small Model Medium Model Large Model
Arduino Nano 33 BLE 100-200ms 300-500ms 1-2s
ESP32 50-100ms 150-300ms 500ms-1s
STM32F4 50-100ms 200-400ms 800ms-1.5s
STM32H7 20-50ms 100-200ms 300-600ms

Memory Requirements

Model Size Original (FP32) Quantized (INT8) Reduction
Small 500 KB 125 KB 75%
Medium 2 MB 500 KB 75%
Large 5 MB 1.25 MB 75%

Power Consumption

Platform Inference Power Idle Power Deep Sleep
Arduino 20-50 mA 5-10 mA < 1 mA
ESP32 80-160 mA 10-20 mA < 1 mA
STM32F4 30-80 mA 3-10 mA < 1 µA

Example Applications

Image Classification

spec = {
    "task_type": "inference",
    "platform": "arduino",
    "model_type": "classification",
    "input_shape": [1, 96, 96, 3],  # RGB image
    "num_classes": 10
}

Sensor Anomaly Detection

spec = {
    "task_type": "adaptive_behavior",
    "platform": "esp32",
    "adaptation_type": "online_learning"
}

Privacy-Preserving Learning

spec = {
    "task_type": "federated_learning",
    "platform": "stm32",
    "aggregation_method": "federated_averaging",
    "privacy_level": "differential_privacy"
}

Architecture

Components

┌─────────────────────────────────────────────┐
│         Accelerapp TinyML Stack             │
├─────────────────────────────────────────────┤
│  TinyMLAgent                                │
│  ├── Inference Generation                   │
│  ├── Model Conversion                       │
│  ├── Federated Learning                     │
│  └── Adaptive Behavior                      │
├─────────────────────────────────────────────┤
│  Firmware Generator (ML Integration)        │
│  ├── ML Code Integration                    │
│  ├── Platform-Specific Optimization         │
│  └── Hardware Abstraction                   │
├─────────────────────────────────────────────┤
│  Platform Support                           │
│  ├── Arduino                                │
│  ├── ESP32                                  │
│  ├── STM32                                  │
│  ├── Nordic                                 │
│  └── Raspberry Pi Pico                      │
└─────────────────────────────────────────────┘

Workflow

┌───────────┐     ┌──────────────┐     ┌─────────────┐
│  Train    │────▶│  Optimize    │────▶│  Generate   │
│  Model    │     │  & Convert   │     │  Code       │
└───────────┘     └──────────────┘     └─────────────┘
                                              │
                                              ▼
                                       ┌─────────────┐
                                       │  Deploy to  │
                                       │  Device     │
                                       └─────────────┘
                                              │
                                              ▼
                                       ┌─────────────┐
                                       │  On-Device  │
                                       │  Inference  │
                                       └─────────────┘

Documentation

Testing

Run the comprehensive test suite:

# Test TinyML agent
pytest tests/test_tinyml_agent.py -v

# Test firmware ML integration
pytest tests/test_firmware_ml_integration.py -v

# Run all tests
pytest tests/ -v

Limitations & Considerations

Memory Constraints

  • Minimum RAM: 64KB (for very small models)
  • Recommended RAM: 256KB or more
  • Tensor arena: 32-128KB depending on model size

Model Size

  • Keep models under 2MB for best compatibility
  • Use quantization to reduce size by ~75%
  • Test on target hardware early

Inference Speed

  • Varies by platform (20ms - 2s)
  • Depends on model complexity
  • Consider using hardware accelerators when available

Power Consumption

  • Inference consumes more power than idle
  • Use sleep modes between inferences
  • Consider battery life in your design

Roadmap

Coming Soon

  • Support for object detection models
  • Image segmentation support
  • Edge TPU and NPU acceleration
  • AutoML integration
  • Model monitoring and drift detection
  • Over-the-air (OTA) model updates
  • Multi-model inference
  • Hardware profiling tools

Future Enhancements

  • ONNX model support
  • Custom layer definitions
  • Advanced pruning strategies
  • Neural architecture search
  • Automatic hardware selection

Contributing

We welcome contributions! Areas where you can help:

  1. Platform Support: Add support for new microcontrollers
  2. Optimization: Improve quantization and pruning algorithms
  3. Examples: Share your TinyML projects
  4. Documentation: Improve guides and tutorials
  5. Testing: Add more test cases

See CONTRIBUTING.md for guidelines.

Resources

Learning

Community

License

TinyML features are part of Accelerapp and are released under the MIT License.

Support

Made with ❤️ by the Accelerapp team

Bringing Machine Learning to the Edge