A heterogeneous ARM integrated toolchain for edge AI performance tuning, enabling developers to compress, optimize, debug and deploy AI models on ARM-based mobile devices beneficial to ARM architecture developers and users.

• Model Compressor: Reduces model size through 4-bit/3-bit/2-bit quantization, structured pruning, and knowledge distillation. Benchmarks latency, memory usage, and power consumption on ARM devices.
• Runtime Inspector: Multi-agent profiling system that monitors CPU/GPU/NPU utilization, tracks thermal conditions, analyzes computation graph bottlenecks, and generates natural language diagnostics explaining performance issues.
• Memory Engine: On-device Retrieval-Augmented Generation (RAG) system that indexes text documents, PDFs, images, and audio files locally. Creates embeddings, stores vectors, and enables semantic search without network connectivity.
• Battery Scheduler: ML-based predictor estimates battery drain for AI tasks. Schedules model inference during optimal power windows and defers tasks when battery is below threshold or device is overheating.
• IoT Layer: Connects ARM Cortex-M/A IoT devices via BLE, Thread, and Matter protocols. Fuses sensor data from accelerometers, gyroscopes, microphones, and biosignal sensors. Runs TinyML models on microcontrollers.
• Privacy Firewall: Sandboxes AI model execution to prevent data leakage. Enforces local-only processing policies, monitors data flow for sensitive information, and validates operations against permission rules.

• Live Site Demo: https://samirasamrose.github.io/arm-adaptive-intelligence/
• Source Code: https://github.com/SamiraSamrose/arm-adaptive-intelligence
• Video Demo: https://youtube.com/shorts/_--l0sK-NQQ

• Programming Languages: Python, Kotlin, Java, Swift, Objective-C, Shell Script
• Mobile Frameworks: Android SDK, iOS SDK, Jetpack Compose, SwiftUI, UIKit
• ML/AI Frameworks: TensorFlow Lite, PyTorch, ONNX Runtime, CoreML, ARM Compute Library
• Quantization Libraries: QLoRA, GPTQ, AWQ, bitsandbytes, PEFT
• Embedding & NLP: Transformers, Sentence-Transformers, Hugging Face Hub
• Computer Vision: OpenCV, Pillow, MobileViT, MobileSAM
• Audio Processing: Librosa, Soundfile, Whisper-tiny
• Vector Database: ChromaDB, Milvus Lite
• IoT Protocols: Bluetooth Low Energy (BLE), Thread, Matter, WiFi
• Build Tools: Gradle, CocoaPods, Xcode, CMake
• Testing: pytest, JUnit, XCTest
• Containerization: Docker, docker-compose
• Web Framework (Bridge): Flask, Flask-CORS
• System Monitoring: psutil, Android BatteryManager, iOS UIDevice
• Scientific Computing: NumPy, SciPy, Pandas, scikit-learn
• Configuration: YAML, JSON
• Data Formats: ONNX, TFLite, CoreML, PyTorch (.pt/.pth)
• Hardware APIs: ARM NEON, ARM SVE, ARM Ethos NPU, Mali GPU, Adreno GPU, Apple Neural Engine
• ML Models: MobileNetV2, ResNet18, BERT-Base, TinyBERT, YOLOv5, Whisper
• Data Integrations: Local file system, Mobile storage, Encrypted cache

• ML Model Compression / Distillation Benchmarks: HuggingFace Open LLM Leaderboard Models, TinyML Perf Dataset (MIT)
• Multimodal Personal Memory (OCR, Speech, Vision) Training: COCO Images, CommonVoice Speech, ArXiv Papers Corpus, DocVQA / IIT-CDIP PDFs
• Performance & Thermal Profiling- MLPerf Mobile, ARM Compute Library Samples
• Battery & Power Modeling- Smartphone Power Consumption Dataset (UMass Trace), WESAD Bio-signal Set (for wearable IoT sensor behavior)

pip install -r requirements.txt
python setup.py install

from src.model_compressor import ModelCompressor
from src.runtime_inspector import RuntimeInspector

# Compress a model
compressor = ModelCompressor()
compressed_model = compressor.compress_model("path/to/model")

# Profile runtime
inspector = RuntimeInspector()
metrics = inspector.profile_inference(compressed_model)

1. Clone the repository

git clone https://github.com/samirasamrose/arm-adaptive-intelligence.git
cd arm-adaptive-intelligence

2. Create virtual environment

python3 -m venv venv
source venv/bin/activate

3. Install dependencies

pip install -r requirements.txt
pip install -e .

4. Run tests

python -m pytest tests/

• Follow PEP 8 for Python code
• Use meaningful variable names
• Add docstrings to all functions
• Comment complex logic

• Write unit tests for new features
• Ensure all tests pass before submitting PR
• Aim for high code coverage

1. Fork the repository
2. Create a feature branch
3. Make your changes
4. Run tests
5. Submit pull request