tinyml-microcontroller-ai

name	tinyml-microcontroller-ai
description	TinyML Microcontroller AI enables deployment of machine learning models Use when this capability is needed.
metadata	{"author":"amnadtaowsoam"}

Tinyml Microcontroller Ai

Skill Profile

(Select at least one profile to enable specific modules)

• DevOps
• Backend
• Frontend
• AI-RAG
• Security Critical

Overview

TinyML Microcontroller AI enables deployment of machine learning models on resource-constrained microcontrollers (MCUs) with limited memory, compute, and power. This capability is essential for edge AI applications requiring offline operation, low latency, and energy efficiency in industrial IoT, smart devices, and embedded systems.

Why This Matters

• Offline Intelligence: Enables AI capabilities without cloud connectivity, critical for remote or offline applications
• Low Latency: Sub-millisecond inference for real-time applications where every millisecond counts
• Energy Efficiency: Battery-powered devices with months/years of operation through power optimization
• Cost Reduction: Eliminates cloud infrastructure and data transfer costs by processing on-device
• Privacy: Data processing on-device without leaving the edge, ensuring data privacy and compliance

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Core Concepts & Rules

1. Core Principles

• Follow established patterns and conventions
• Maintain consistency across codebase
• Document decisions and trade-offs

2. Implementation Guidelines

• Start with the simplest viable solution
• Iterate based on feedback and requirements
• Test thoroughly before deployment

Inputs / Outputs / Contracts

• Inputs:
  • Trained TensorFlow/Keras model
  • Representative dataset for quantization
  • Target hardware specifications (RAM, Flash, compute)
  • Performance requirements (latency, power)
• Entry Conditions:
  • TensorFlow/Keras model trained and validated
  • Target microcontroller selected and available
  • Development environment set up (PlatformIO, STM32CubeIDE, etc.)
  • Hardware constraints understood and documented
• Outputs:
  • Quantized TFLite model file (.tflite)
  • C header file with model data
  • Inference implementation code
  • Performance metrics (latency, memory, power)
• Artifacts Required (Deliverables):
  • TFLite model file
  • C header file (model_data.cc/h)
  • Inference class implementation
  • Main application code
  • Performance benchmark results
• Acceptance Evidence:
  • Inference latency measurement (< 100ms)
  • Memory usage report (RAM/Flash)
  • Power consumption measurement (< 10mW)
  • Accuracy comparison with original model (> 95%)
• Success Criteria:
  • Model size fits in flash memory
  • Tensor arena fits in RAM
  • Inference latency meets requirements (< 100ms)
  • Power consumption within budget (< 10mW)
  • Accuracy within acceptable range (> 95% of cloud model)

Skill Composition

• Depends on: edge-model-compression (Model optimization techniques)
• Compatible with: hybrid-inference-architecture, on-device-model-training
• Conflicts with: None
• Related Skills: edge-ai-development-workflow, model-optimization-quantization

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Quick Start / Implementation Example

1. Review requirements and constraints
2. Set up development environment
3. Implement core functionality following patterns
4. Write tests for critical paths
5. Run tests and fix issues
6. Document any deviations or decisions

# Example implementation following best practices
def example_function():
    # Your implementation here
    pass

Assumptions / Constraints / Non-goals

• Assumptions:
  • Development environment is properly configured
  • Required dependencies are available
  • Team has basic understanding of domain
• Constraints:
  • Must follow existing codebase conventions
  • Time and resource limitations
  • Compatibility requirements
• Non-goals:
  • This skill does not cover edge cases outside scope
  • Not a replacement for formal training

Compatibility & Prerequisites

• Supported Versions:
  • Python 3.8+
  • Node.js 16+
  • Modern browsers (Chrome, Firefox, Safari, Edge)
• Required AI Tools:
  • Code editor (VS Code recommended)
  • Testing framework appropriate for language
  • Version control (Git)
• Dependencies:
  • Language-specific package manager
  • Build tools
  • Testing libraries
• Environment Setup:
  • .env.example keys: API_KEY, DATABASE_URL (no values)

Test Scenario Matrix (QA Strategy)

Type	Focus Area	Required Scenarios / Mocks
Unit	Core Logic	Must cover primary logic and at least 3 edge/error cases. Target minimum 80% coverage
Integration	DB / API	All external API calls or database connections must be mocked during unit tests
E2E	User Journey	Critical user flows to test
Performance	Latency / Load	Benchmark requirements
Security	Vuln / Auth	SAST/DAST or dependency audit
Frontend	UX / A11y	Accessibility checklist (WCAG), Performance Budget (Lighthouse score)

Technical Guardrails & Security Threat Model

1. Security & Privacy (Threat Model)

• Top Threats: Injection attacks, authentication bypass, data exposure

• Data Handling: Sanitize all user inputs to prevent Injection attacks. Never log raw PII
• Secrets Management: No hardcoded API keys. Use Env Vars/Secrets Manager
• Authorization: Validate user permissions before state changes

2. Performance & Resources

• Execution Efficiency: Consider time complexity for algorithms
• Memory Management: Use streams/pagination for large data
• Resource Cleanup: Close DB connections/file handlers in finally blocks

3. Architecture & Scalability

• Design Pattern: Follow SOLID principles, use Dependency Injection
• Modularity: Decouple logic from UI/Frameworks

4. Observability & Reliability

• Logging Standards: Structured JSON, include trace IDs request_id
• Metrics: Track error_rate, latency, queue_depth
• Error Handling: Standardized error codes, no bare except
• Observability Artifacts:
  • Log Fields: timestamp, level, message, request_id
  • Metrics: request_count, error_count, response_time
  • Dashboards/Alerts: High Error Rate > 5%

Agent Directives & Error Recovery

(ข้อกำหนดสำหรับ AI Agent ในการคิดและแก้ปัญหาเมื่อเกิดข้อผิดพลาด)

• Thinking Process: Analyze root cause before fixing. Do not brute-force.
• Fallback Strategy: Stop after 3 failed test attempts. Output root cause and ask for human intervention/clarification.
• Self-Review: Check against Guardrails & Anti-patterns before finalizing.
• Output Constraints: Output ONLY the modified code block. Do not explain unless asked.

Definition of Done (DoD) Checklist

• Tests passed + coverage met
• Lint/Typecheck passed
• Logging/Metrics/Trace implemented
• Security checks passed
• Documentation/Changelog updated
• Accessibility/Performance requirements met (if frontend)

Anti-patterns / Pitfalls

• ⛔ Don't: Log PII, catch-all exception, N+1 queries
• ⚠️ Watch out for: Common symptoms and quick fixes
• 💡 Instead: Use proper error handling, pagination, and logging

Reference Links & Examples

• Internal documentation and examples
• Official documentation and best practices
• Community resources and discussions

Versioning & Changelog

• Version: 1.0.0
• Changelog:
  • 2026-02-22: Initial version with complete template structure

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