Skip to content

TINYML_INTEGRATION.md

Latest commit

 

History

History
433 lines (301 loc) · 10.4 KB

TINYML_INTEGRATION.md

File metadata and controls

433 lines (301 loc) · 10.4 KB

TinyML Integration Guide

Overview

Accelerapp now includes comprehensive TinyML (Tiny Machine Learning) and Edge AI capabilities, enabling on-device machine learning and federated learning for microcontrollers and embedded systems.

Features

1. TinyML Agent

The TinyMLAgent provides specialized capabilities for integrating machine learning models into embedded systems:

  • On-Device Inference: Generate optimized neural network code for microcontrollers
  • Model Optimization: Quantization, pruning, and compression for edge deployment
  • Federated Learning: Privacy-preserving distributed learning infrastructure
  • Adaptive Behavior: Online learning and environment adaptation

2. Supported Platforms

TinyML integration works with the following platforms:

  • Arduino: Nano 33 BLE, Portenta H7, Nano RP2040
  • ESP32: ESP32, ESP32-S3
  • STM32: STM32F4, STM32F7, STM32H7
  • Nordic: nRF52840, nRF5340
  • Raspberry Pi Pico: RP2040

3. Optimization Techniques

  • Quantization: Convert float32 models to int8 (4x size reduction)
  • Pruning: Remove unnecessary weights
  • Knowledge Distillation: Create smaller models from larger ones
  • Weight Sharing: Reduce memory footprint

Getting Started

Basic Usage

from accelerapp.agents import TinyMLAgent

# Initialize the agent
agent = TinyMLAgent()

# Check if agent can handle a task
if agent.can_handle("tinyml inference"):
    print("TinyML agent ready!")

Inference Code Generation

Generate optimized inference code for your microcontroller:

spec = {
    "task_type": "inference",
    "platform": "arduino",
    "model_type": "classification",
    "input_shape": [1, 28, 28, 1],  # e.g., 28x28 grayscale image
    "num_classes": 10,
}

result = agent.generate(spec)

# Access generated files
header_code = result["files"]["ml_inference.h"]
impl_code = result["files"]["ml_inference.c"]

# Check memory requirements
memory = result["memory_estimate"]
print(f"Memory required: {memory['total_estimated']}")

Model Conversion

Convert and optimize your trained model for edge deployment:

spec = {
    "task_type": "model_conversion",
    "platform": "esp32",
    "model_path": "/path/to/your/model.h5",
    "optimization_level": "aggressive",  # or "standard", "conservative"
}

result = agent.generate(spec)

# Conversion results
print(f"Size reduction: {result['size_reduction']}")
print(f"Output format: {result['output_format']}")

Federated Learning

Set up federated learning infrastructure:

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

result = agent.generate(spec)

# Generated files include:
# - federated_learning.h: API definitions
# - federated_learning.c: Implementation

Adaptive Behavior

Enable online learning and adaptive behavior:

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

result = agent.generate(spec)

# Features:
# - Real-time parameter updates
# - Environment adaptation
# - Resource-aware execution

API Reference

TinyMLAgent Class

Constructor

agent = TinyMLAgent()

Initializes the TinyML agent with default capabilities.

Methods

can_handle(task: str) -> bool

Check if the agent can handle a specific task.

Parameters:

  • task: Task description string

Returns:

  • True if the agent can handle the task, False otherwise

Example:

if agent.can_handle("neural network inference"):
    # Agent can handle this task
    pass
generate(spec: Dict[str, Any], context: Dict[str, Any] = None) -> Dict[str, Any]

Generate TinyML code based on specification.

Parameters:

  • spec: Specification dictionary with the following keys:

    • task_type: One of "inference", "model_conversion", "federated_learning", "adaptive_behavior"
    • platform: Target platform (e.g., "arduino", "esp32", "stm32")
    • Additional task-specific parameters

  • context: Optional additional context

Returns:

  • Dictionary containing:
    • status: "success" or "error"
    • files: Generated code files
    • memory_estimate: Memory requirements
    • performance_estimate: Performance metrics
    • Task-specific results

Example:

result = agent.generate({
    "task_type": "inference",
    "platform": "arduino",
    "model_type": "classification",
})
get_capabilities() -> List[str]

Get list of agent capabilities.

Returns:

  • List of capability strings
get_info() -> Dict[str, Any]

Get agent information.

Returns:

  • Dictionary with agent metadata

Integration with Orchestrator

The TinyML agent integrates seamlessly with the agent orchestrator:

from accelerapp.agents import AgentOrchestrator, TinyMLAgent

orchestrator = AgentOrchestrator()
tinyml_agent = TinyMLAgent()
orchestrator.register_agent(tinyml_agent)

# Find agent for TinyML tasks
agent = orchestrator.find_agent("tinyml")

Code Examples

Example 1: Image Classification on Arduino

from accelerapp.agents import TinyMLAgent

agent = TinyMLAgent()

# Generate inference code for image classification
spec = {
    "task_type": "inference",
    "platform": "arduino",
    "model_type": "classification",
    "input_shape": [1, 96, 96, 1],  # 96x96 grayscale
    "num_classes": 3,  # e.g., cat, dog, bird
}

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']}")

Example 2: Sensor Data Processing with Adaptive Learning

from accelerapp.agents import TinyMLAgent

agent = TinyMLAgent()

# Generate adaptive behavior for sensor processing
spec = {
    "task_type": "adaptive_behavior",
    "platform": "esp32",
    "adaptation_type": "online_learning",
}

result = agent.generate(spec)

# The generated code includes:
# - adaptive_init(): Initialize learning system
# - adaptive_update(): Update model with new data
# - adaptive_predict(): Make predictions

Example 3: Privacy-Preserving Federated Learning

from accelerapp.agents import TinyMLAgent

agent = TinyMLAgent()

# Setup federated learning for multiple devices
spec = {
    "task_type": "federated_learning",
    "platform": "stm32",
    "aggregation_method": "federated_averaging",
    "privacy_level": "differential_privacy",
}

result = agent.generate(spec)

# Generated code provides:
# - Local training on device data
# - Gradient aggregation without sharing raw data
# - Privacy-preserving model updates

Memory and Performance Considerations

Memory Requirements

Typical memory requirements by platform:

Platform RAM Flash Tensor Arena
Arduino Nano 33 BLE 256 KB 1 MB 32 KB
ESP32 520 KB 4 MB 64 KB
STM32F4 192 KB 1 MB 32 KB
STM32H7 1 MB 2 MB 128 KB

Model Size Guidelines

  • Original model: 0.5-5 MB (float32)
  • Quantized model: 0.125-1.25 MB (int8, 4x reduction)
  • Tensor arena: 32-128 KB (runtime memory)

Performance Estimates

Inference times for typical models:

Platform Small Model Medium Model Large Model
Arduino 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

Best Practices

1. Model Optimization

  • Always quantize models to int8 before deployment
  • Use pruning to reduce model size
  • Test on target hardware early in development

2. Memory Management

  • Choose appropriate tensor arena size
  • Monitor RAM usage during inference
  • Use static allocation when possible

3. Power Efficiency

  • Enable sleep modes between inferences
  • Use hardware accelerators when available
  • Batch predictions to reduce overhead

4. Federated Learning

  • Minimize communication frequency
  • Use differential privacy to protect user data
  • Validate aggregated models before deployment

5. Testing

  • Test with real sensor data
  • Validate model accuracy on device
  • Monitor inference latency and memory usage

Troubleshooting

Common Issues

Issue: Out of memory during inference

  • Solution: Reduce model size or increase tensor arena

Issue: Slow inference times

  • Solution: Use quantization and pruning, or upgrade to faster platform

Issue: Model accuracy degraded after quantization

  • Solution: Use quantization-aware training or increase bit width

Issue: Communication failures in federated learning

  • Solution: Check network connectivity and retry logic

Advanced Topics

Custom Model Architectures

The TinyML agent generates template code that can be customized for specific model architectures:

// Custom layer implementation
void custom_layer_forward(const float* input, float* output, int size) {
    // Your custom layer logic
}

TensorFlow Lite Micro Integration

For advanced use cases, integrate with TensorFlow Lite Micro:

#define USE_TFLITE
#include "ml_inference.h"
// TFLite Micro code will be used

Hardware Acceleration

On supported platforms, enable hardware acceleration:

// Use ARM CMSIS-NN for acceleration
#define USE_CMSIS_NN

Future Enhancements

Planned features for future releases:

  • Support for more model types (object detection, segmentation)
  • AutoML integration for model architecture search
  • Edge TPU and NPU support
  • Improved federated learning protocols
  • Model monitoring and drift detection

Resources

Contributing

We welcome contributions to improve TinyML integration:

  1. Report issues on GitHub
  2. Submit pull requests with new features
  3. Share your TinyML projects and use cases

License

TinyML integration is part of Accelerapp and is released under the MIT License.