# Continual Learning on Edge Microcontrollers: TinyML Thesis
> Explore a research proposal evaluating lightweight streaming AI methods for concept drift recovery on Arm Cortex-M4 microcontrollers using TinyML.

Tags: tinyml, continual-learning, microcontrollers, edge-ai, concept-drift, streaming-ai, embedded-systems
## Research Objective
* **Problem:** Identifying effective lightweight continual-learning methods for maintaining predictive performance under concept drift on microcontroller-class hardware.
* **Key Metrics:** Trade-offs between accuracy, memory (RAM/Flash), latency, and energy consumption.
* **Target Platform:** Arm Cortex-M4 (Arduino Nano 33 BLE Sense with 256 KB SRAM).

## Proposed Analysis Tools & Methods
* **Online SGD (Perceptron):** Single-layer linear model, ~10 KB RAM usage.
* **Experience Replay:** Fixed-size ring buffer (50–100 samples), +100 KB RAM usage.
* **EWC-Lite:** Elastic Weight Consolidation for weight regularization.
* **Evaluation Dataset:** UCI HAR (Accelerometer time-series at 50 Hz).

## Project Timeline
* **Month 1-2:** Literature Review, Gap Analysis, and Algorithm Design.
* **Month 3-4:** Dataset Preparation and C/C++ Implementation on MCU.
* **Month 5-6:** Experiment Execution, Data Analysis, and Thesis Writing.

## Expected Contributions
* **Benchmark Framework:** A public suite for evaluating TinyML continual learning.
* **Empirical Data:** Concrete performance measurements (e.g., Perceptron at ~70% accuracy vs. Replay Buffer at ~85% accuracy).
* **Design Guidelines:** Practical recommendations for developers working within hardware constraints (e.g., RAM-specific algorithm selection).
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