🚀 AI Edge Computing & TinyML

Comprehensive Guide to State-of-the-Art Edge AI

---

🌟 Latest Update: January 2025

Production-Ready Python Implementation with modern tooling (Hatch, Ruff, Mypy) 62/62 Tests Passing • 81.76% Coverage • Zero Security Issues State-of-the-Art Algorithms & Trends for Edge AI and Embedded Systems

---

📋 Table of Contents

🚀 Getting Started 📦 Installation 🛠️ Development Setup 📊 Project Structure ✅ Quality Assurance 🎯 Features & Examples	🔥 Core Topics 🎯 SOTA Models 2024-2025 👁️ Object Detection 🤖 Small Language Models ⚡ State Space Models	🛠️ Frameworks & Tools 🚀 Inference Frameworks 🔧 Model Optimization 💻 Hardware Platforms 🌐 Deployment Tools
📚 Documentation 📄 CHANGELOG.md 📚 LESSONS-LEARNED.md 🔧 DEVELOPMENT.md	📚 Resources 🎯 TinyML & MCU ⚙️ Compilers 📄 Research Papers	🎓 Community 🤝 Contributing 📊 Repository Stats 🏷️ Keywords

---

🚀 Quick Start & Development

📦 Installation

This project uses modern Python tooling with Hatch for dependency management and development workflows.

# Clone the repository
git clone https://github.com/umitkacar/ai-edge-computing-tiny-embedded.git
cd ai-edge-computing-tiny-embedded

# Install dependencies (using hatch)
pip install hatch

# Run tests
hatch run test

# Run full CI pipeline
hatch run ci

🛠️ Development Setup

Modern Python Stack:

• Build System: Hatch - Modern Python project manager
• Linting: Ruff - Ultra-fast Python linter (100x faster than flake8)
• Formatting: Black - The uncompromising code formatter
• Type Checking: Mypy - Static type checker (strict mode)
• Testing: Pytest - Comprehensive test framework
• Security: Bandit - Security vulnerability scanner
• Pre-commit: Automated quality checks on commit/push

Available Commands:

# Linting & Formatting
hatch run lint          # Run Ruff linter
hatch run format        # Format code with Black
hatch run format-check  # Check formatting without changes

# Type Checking
hatch run type-check    # Run Mypy strict type checking

# Testing
hatch run test                    # Run tests (sequential)
hatch run test-parallel           # Run tests with auto workers
hatch run test-parallel-cov       # Parallel tests with coverage

# Security
hatch run security      # Run Bandit security audit

# Complete CI Pipeline
hatch run ci           # Run all checks (format, lint, type-check, security, test)

📊 Project Structure

ai-edge-computing-tiny-embedded/
├── src/ai_edge_tinyml/          # Source code (src layout)
│   ├── __init__.py              # Package initialization
│   ├── quantization.py          # INT8/INT4/FP16 quantization
│   ├── model_optimizer.py       # Model optimization pipeline
│   ├── utils.py                 # Utility functions
│   └── py.typed                 # PEP 561 marker (typed package)
├── tests/                       # Test suite (62 tests, 81.76% coverage)
│   ├── conftest.py              # Pytest configuration & fixtures
│   ├── test_quantization.py     # Quantization tests (21 tests)
│   ├── test_model_optimizer.py  # Optimizer tests (19 tests)
│   └── test_utils.py            # Utility tests (22 tests)
├── pyproject.toml               # Project configuration (single source of truth)
├── .pre-commit-config.yaml      # Pre-commit hooks configuration
├── CHANGELOG.md                 # Detailed change history
├── LESSONS-LEARNED.md           # Best practices & insights
├── DEVELOPMENT.md               # Development guidelines
└── README.md                    # This file

✅ Quality Assurance

This project maintains production-ready code quality:

Check	Status	Details
Ruff Linting	✅ PASS	50+ rules, zero errors
Black Formatting	✅ PASS	Line length: 100
Mypy Type Check	✅ PASS	Strict mode enabled
Bandit Security	✅ PASS	0 vulnerabilities
Test Suite	✅ PASS	62/62 tests passing
Code Coverage	✅ PASS	81.76% (exceeds 80%)
Pre-commit Hooks	✅ PASS	15+ automated checks

Test Results:

tests/test_quantization.py      21 passed
tests/test_model_optimizer.py   19 passed
tests/test_utils.py             22 passed
━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━
Total: 62 passed in 0.50s ✅
Coverage: 81.76% (exceeds 80% threshold) ✅

🔒 Security

• Bandit Security Audit: Zero vulnerabilities detected
• Type Safety: Full type annotations with mypy strict mode
• Dependency Scanning: Automated security checks in CI
• Pre-commit Hooks: Security validations before commit

📚 Documentation

• CHANGELOG.md - Detailed version history and changes
• LESSONS-LEARNED.md - Best practices, insights, and technical decisions
• DEVELOPMENT.md - Comprehensive development guidelines
• API Documentation: Auto-generated from Google-style docstrings

🎯 Features

Quantization Support:

• ✅ INT8 Quantization (8-bit integers)
• ✅ INT4 Quantization (4-bit integers)
• ✅ FP16 Quantization (16-bit floats)
• ✅ Dynamic Quantization
• ✅ Symmetric & Asymmetric modes
• ✅ Per-tensor & per-channel quantization

Model Optimization:

• ✅ Weight quantization with 6 different modes
• ✅ Compression ratio analysis
• ✅ Model size calculation
• ✅ Type-safe APIs with full annotations
• ✅ Comprehensive error handling

Example Usage:

import numpy as np
from ai_edge_tinyml import Quantizer, QuantizationConfig, QuantizationMode

# Create quantization config
config = QuantizationConfig(
    mode=QuantizationMode.INT8,
    symmetric=True,
    per_channel=False
)

# Initialize quantizer
quantizer = Quantizer(config)

# Quantize weights
weights = np.random.randn(100, 100).astype(np.float32)
quantized = quantizer.quantize(weights)

# Dequantize for inference
dequantized = quantizer.dequantize(quantized)

# Calculate compression
from ai_edge_tinyml.utils import calculate_compression_ratio
ratio = calculate_compression_ratio(weights, quantized)
print(f"Compression ratio: {ratio:.2f}x")

---

🔥 SOTA Models & Algorithms (2024-2025)

---

🎯 Object Detection Models

🥇 YOLOv11 (YOLO11) 🚀 State-of-the-art real-time object detection with transformer-based improvements ✨ Key Features: ⚡ Transformer-based backbone with C3k2 blocks 🎯 Partial Self-Attention (PSA) mechanism 🔥 NMS-free training with dual label assignment 📉 25-40% lower latency vs YOLOv10 📊 10-15% improvement in mAP ⚡ 60+ FPS processing capability 📚 Resources: 📖 Ultralytics Docs → https://docs.ultralytics.com/models/ 📄 YOLO Evolution → https://arxiv.org/html/2510.09653v2

🥈 YOLOv10 ⚡ Eliminates NMS for end-to-end real-time detection 📊 Performance Metrics: 🔸 YOLOv10s: 1.8x faster than RT-DETR-R18 🔸 YOLOv10b: 46% less latency, 25% fewer parameters 🔸 mAP Range: 38.5 - 54.4 📚 Resources: 📄 Paper → https://arxiv.org/pdf/2405.14458 📖 Docs → https://docs.ultralytics.com/models/yolov10/

---

🤖 RT-DETR & RT-DETRv2

🎯 First practical real-time detection transformer

Model	AP Score	FPS	Device
RT-DETR	53.1%	108	NVIDIA T4
RT-DETRv2	>55%	108+	NVIDIA T4

🔗 Resources:

• 📊 RT-DETR vs YOLO11 Comparison

---

📱 Efficient Vision Models for Edge

graph LR
    A[🖼️ Input Image] --> B[📱 MobileNetV4]
    A --> C[⚡ EfficientViT]
    B --> D[🎯 87% Accuracy]
    C --> E[🔥 3.8ms Latency]
    D --> F[📲 Edge TPU]
    E --> F
    style A fill:#e1f5ff
    style B fill:#ffe1f5
    style C fill:#f5ffe1
    style D fill:#ffe1e1
    style E fill:#e1ffe1
    style F fill:#ffd700

Loading

---

📱 MobileNetV4 🌐 Universal efficient architecture for mobile ecosystem 🎨 Innovations: 🔹 Universal Inverted Bottleneck (UIB) block ⚡ Mobile MQA attention (39% speedup) 🎯 Optimized NAS recipe 🏆 87% ImageNet accuracy @ 3.8ms (Pixel 8 EdgeTPU) 📚 Resources: 📄 MobileNetV4 Paper (Springer) 🔬 Google Research

⚡ EfficientViT 🧠 Lightweight multi-scale attention for high-resolution tasks ✨ Features: 🔸 Memory-efficient Vision Transformer 🔸 Cascaded group attention 🔸 Dense prediction tasks optimized 🔸 High-resolution image processing

---

🤖 Small Language Models (SLMs) for Edge

---

🧠 Microsoft Phi-3 📊 Variants: Model: Phi-3-mini Parameters: 3.8B Context: Up to 128K tokens Deployment: GPU, CPU, Mobile Status: ✅ Production Ready 🎯 Optimized For: 💻 GPU acceleration 🖥️ CPU inference 📱 Mobile deployment 🔗 Resources: Phi-3 Overview	🦙 TinyLlama 📊 Specifications: Parameters: 1.1B Target: Mobile/Edge devices Performance: High for size class Year: 2024 Status: ✅ Active ✨ Highlights: 🔸 Compact architecture 🔸 Edge-optimized 🔸 Strong performance/size ratio
🌟 Google Gemini Nano 📱 On-device AI for Smartphones Variants: 📊 1.8B parameters (lightweight) 📊 3.25B parameters (standard) 🎯 Capabilities: ✅ Context-aware reasoning ✅ Real-time translation ✅ Text summarization ✅ Edge-optimized for phones/IoT	🦙 Meta Llama 3.2 🖼️ Edge AI & Vision Capabilities Features: ⚡ Edge deployment optimized 👁️ Vision-language capabilities 📱 Mobile-friendly variants 🔥 Latest architecture 🔗 Resources: Llama 3.2 Announcement

---

📷 MobileVLM

🎨 Efficient vision-language model for mobile devices

Specifications:

• 🔹 mobileLLaMA: 2.7B parameters
• 🔹 Trained from scratch on open datasets
• 🔹 Fully optimized for mobile deployment
• 🔹 Vision + Language capabilities

---

⚡ State Space Models - Efficient Transformers

---

🐍 Mamba ⚡ Linear-time sequence modeling with selective state spaces 🚀 Performance Highlights: Metric Performance Throughput 5x higher than Transformers Scaling Linear in sequence length Comparison Mamba-3B > Transformers (same size) Power Matches Transformers 2x its size 📊 Advantages: + ✅ Linear time complexity + ✅ 5x throughput improvement + ✅ Efficient long sequences + ✅ Lower memory footprint - ❌ Newer architecture (less tested) 📚 Resources: 📄 Mamba Paper 💻 Mamba GitHub 📖 Mamba Survey

📱 eMamba 🔧 Edge-optimized Mamba acceleration framework ✨ Features: Design: End-to-end hardware acceleration Target: Edge platforms Complexity: Linear time Status: 2024 Release 🎯 Optimizations: 🔹 Hardware-aware design 🔹 Edge platform specific 🔹 Leverages linear complexity 🔹 Memory efficient 📚 Resources: 📄 eMamba Paper

---

🚀 Inference Frameworks & Runtimes

---

⚡ TensorRT-LLM 🏆 High-performance LLM inference on NVIDIA GPUs 📊 Performance: + 70% faster than llama.cpp on RTX 4090 + State-of-the-art optimizations + Quality maintained across precisions ✨ Features: 🔸 Python & C++ API 🔸 Multi-precision support 🔸 Advanced kernel optimization 🔸 Production-grade quality 🔗 Resources: TensorRT-LLM GitHub Deployment Guide	📄 vLLM 💡 High-throughput LLM serving with PagedAttention 🎯 Innovations: ⚡ PagedAttention memory management 🔸 Optimized KV cache handling 🌐 Multi-platform support 🖥️ Supported Hardware: AMD: GPU support Google: TPU support AWS: Inferentia support Base: PyTorch 🔗 Resources: vLLM vs TensorRT-LLM
🦙 ExecuTorch 📱 Efficient LLM execution on edge devices Features: 🔹 Lightweight edge runtime 🔹 Static memory planning 🔹 Multi-platform support 🔹 TorchAO quantization 💻 Hardware Support: ✅ CPU ✅ GPU ✅ AI Accelerators ✅ Mobile devices 🔗 Resources: PyTorch Conference 2024	💻 llama.cpp ⚡ CPU-optimized LLM inference Advantages: + ✅ Lower memory usage + ✅ No GPU required + ✅ Fast generation + ✅ Cross-platform + ✅ Wide model support 🔗 Comparison: vLLM vs Ollama vs llama.cpp vs TGI vs TensorRT-LLM

---

🔧 Model Compression & Optimization

---

📉 Advanced Quantization Techniques

🏆 AWQ Activation-aware Weight Quantization 🎯 MIT HAN Lab Innovation Key Concept: # Not all weights are equal! if is_salient(weight): skip_quantization() else: quantize_weight() Features: ⚡ Protects critical weights 🎯 Activation-aware 🔥 State-of-the-art results 🔗 Resources: AWQ GitHub MIT HAN Lab

💎 GPTQ GPU-Focused Quantization Features: 🔸 Row-wise quantization 🔸 Hessian optimization 🔸 GPU inference focused 🔸 175B models supported Achievements: Models: BLOOM, OPT-175B Precision: 4-bit Platform: GPU optimized

🔬 QLoRA Efficient Fine-tuning Innovations: ✨ 4-bit NormalFloat (NF4) ✨ Double quantization ✨ LoRA adapters ✨ Single GPU fine-tuning Capability: + Fine-tune 65B model + On single GPU + Maintain quality

---

🆕 Unsloth Dynamic 4-bit

🔥 Latest quantization innovation

Features:

• Built on BitsandBytes
• Dynamic parameter quantization
• Per-parameter optimization

📚 Comprehensive Guides:

• 📖 Quantization Comparison
• 📊 GPTQ vs GGUF vs AWQ

---

🔬 Neural Architecture Search (NAS)

🤖 Automate neural network architecture design

🎯 Once-for-All (OFA)

Concept: Train once, deploy everywhere

graph TD
    A[🌐 Supernet Training] --> B[📦 Weight Sharing]
    B --> C[📱 Mobile]
    B --> D[💻 Desktop]
    B --> E[⚡ Edge]
    style A fill:#e1f5ff
    style B fill:#ffe1f5
    style C fill:#f5ffe1
    style D fill:#ffe1e1
    style E fill:#ffd700

Loading

Features:

• 🔹 Weight-sharing supernetwork
• 🔹 Represents any architecture in search space
• 🔹 Massive computational savings
• 🔹 Applied to ImageNet with ProxylessNAS & MobileNetV3

🔗 Resources:

• NAS Overview
• MIT HAN Lab NAS

---

🎓 Knowledge Distillation & Pruning

🔬 TinyBERT 📚 Two-stage distillation approach Performance Metrics: Accuracy: 96.8% of BERT-base Size: 7.5x smaller (4 layers) Energy: Lowest variability (0.1032 kWh SD) Stages: Task-agnostic + Task-specific Advantages: ✅ Dual-stage distillation ✅ Ultra-low energy variability ✅ Compact architecture ✅ High performance retention

📖 DistilBERT ⚡ Single-phase task-agnostic distillation Performance Metrics: Accuracy: 97% of BERT Size Reduction: 40% smaller Speed: 60% faster Use Case: General-purpose Recent Research (2025): 🔸 32% energy reduction with pruning 🔸 Iterative distillation + adaptive pruning 🔸 Nature Scientific Reports

📚 Resources:

• Nature Scientific Reports 2025
• DistilBERT Medium

---

🎯 TinyML & MCU-specific Advances

---

🧠 MCUNet Series - MIT HAN Lab

📱 MCUNetV1 Foundation: 🔸 Neural architecture for MCUs 🔸 Co-designed model + inference engine 🔸 Ultra-low memory footprint

🚀 MCUNetV2 Achievements: ImageNet: 71.8% accuracy Visual Wake: >90% (32kB SRAM) Capability: Object detection Platform: Tiny devices

⚡ MCUNetV3 Latest: 🔸 Enhanced efficiency 🔸 State-of-the-art MCU AI 🔸 Production ready

---

🎓 Additional MCU Tools

🔧 TinyTL Tiny transfer learning for MCUs On-device learning capabilities Minimal resource overhead

⚙️ PockEngine Inference engine optimization MCU-specific acceleration Memory-efficient execution

📚 Resources:

• 🌐 MCUNet Official
• 💻 MCUNet GitHub
• 📖 TinyML Projects

---

🔬 TinyDL (Tiny Deep Learning)

🎯 Evolution from TinyML to deep learning on edge

Focus Areas:

• 🔹 Deep learning on ultra-constrained hardware
• 🔹 Power consumption in mW range
• 🔹 On-device sensor analytics
• 🔹 Real-time inference

📄 Resources:

• TinyDL Survey

---

🔩 Hardware Acceleration & Platforms

---

🖥️ Edge AI Platforms

🟢 NVIDIA Jetson Orin Nano Super Specifications: Compute: 67 INT8 TOPS Performance: 1.7x vs previous Orin Price: $249 Release: Late 2024 Status: ✅ Available Features: ⚡ Generative AI optimized 🎯 Edge AI development kit 💰 Affordable price point

🔷 Edge TPU & Neural Accelerators Hardware Platforms: Google Pixel EdgeTPU Coral Dev Board Apple Neural Engine A-series chips Specialized NPUs Custom ASICs

---

📱 Mobile Deployment Targets

Platform	Architecture	Use Case
🔧 ARM CPUs	ARM Cortex	General compute
📡 Mobile DSPs	Qualcomm/MediaTek	Signal processing
🎮 Mobile GPUs	Mali/Adreno	Graphics + AI
🧠 NPUs	Custom ASICs	Neural processing

---

🛠️ Implementation Resources & Tools

---

🔷 ONNX Runtime

Cross-platform inference with ONNX models

📚 Documentation & Tutorials 📖 ONNX Runtime C++ Inference 🐍 PyTorch to ONNX Tutorial 📝 ONNX Registry Tutorial 🎓 On-Device Training 🔧 Compatibility ⚙️ ONNX Runtime Compatibility 📋 ONNX Versioning 🚀 CUDA Execution Provider

💻 Example Implementations 🖼️ C++ ResNet Console App ⚡ ONNX Runtime C++ Example 🤖 ONNX Runtime Android 🎯 ByteTrack ONNX Inference 📦 Model Repositories 🤗 HuggingFace ONNX Models 🔧 txtai ONNX Pipeline 📤 Ultralytics Export

---

📉 ONNX Runtime Quantization

Tools & Resources:

• 🔧 Quantization Tools
• 📊 Float16 Optimization
• 💡 Quantization Examples

---

🎯 YOLO Implementations

🔥 Click to expand YOLO implementations

🟣 YOLO-NAS with ONNX

• 💻 YOLO-NAS ONNXRuntime

🟢 YOLO + TensorRT (Detection, Pose, Segmentation)

• ⚡ YOLOv8-TensorRT-CPP
• 🔧 TensorRT C++ API
• 🐍 YOLOv8-TensorRT (Python + C++)
• 🤸 YOLO Pose C++
• 📚 TensorRT Samples
• 📺 YOLOv8 TensorRT Tutorial

🔵 YOLO + ONNXRuntime (All Tasks)

• 💻 YOLOv8-ONNX-CPP
• 🤸 YOLOv8 Pose Implementation
• ⚡ YOLOv8 TensorRT Pose
• 🔧 YOLO-ONNXRuntime-CPP
• 📷 YOLOv8-OpenCV-ONNXRuntime-CPP
• 📖 Ultralytics YOLOv8 C++
• 🎯 YOLOv6-OpenCV-ONNXRuntime
• 🏃 YOLOv5 Pose OpenCV

🌐 Community Resources

• 👨‍💻 hpc203 Repositories
• 💬 YOLO Issue Discussions
• 🐛 YOLOv5 Fixed Bugs
• 🇨🇳 Chinese Tutorial
• 📦 ONNX Runtime Install Guide

---

⚡ TensorRT

🚀 NVIDIA's high-performance deep learning inference optimizer

Resources:

• 🔧 TensorRT Execution Provider
• 💾 TensorRT Engine Cache

---

🌐 Edge Deployment Frameworks

---

🚀 FastDeploy - PaddlePaddle 📦 Easy-to-use deployment toolbox for AI models Resources: 💻 FastDeploy GitHub 📥 Prebuilt Libraries 💎 DeepSparse & SparseML - Neural Magic 🖥️ CPU-optimized inference with sparsity Features: ⚡ CPU inference acceleration 🔸 Sparsity-aware optimization 📊 YOLOv5 CPU benchmarks Resources: 📈 YOLOv5 CPU Benchmark 💻 SparseML GitHub 🚀 DeepSparse GitHub

📱 NCNN - Tencent 🎯 High-performance neural network inference for mobile Resources: 💻 NCNN GitHub 📖 NCNN C++ Usage 📱 YoloMobile ⭐ Awesome NCNN 🔄 Model Converter 🔧 MACE - Xiaomi 🤖 Mobile AI Compute Engine Resources: 💻 MACE GitHub

---

🍎 CoreML - Apple

🎨 Machine learning framework for iOS/macOS

📦 Click to expand CoreML resources

🎨 Model Collections

• 🎯 Semantic Segmentation CoreML
• 📚 CoreML Models Collection
• ⭐ Awesome CoreML Models
• 🧠 Awesome CoreML Models 2
• 🎬 RobustVideoMatting

🛠️ Tools & Documentation

• 🔄 PyTorch to CoreML
• 🔧 CoreML Helpers
• 📖 Apple ML API
• 📊 CoreML Performance Tool

🎨 Stable Diffusion on CoreML

• 🔬 Apple ML-4M
• 🎯 Apple ML Stable Diffusion
• 📦 Stable Diffusion 2 Base
• 🚀 Stability AI SD
• 📚 Stable Diffusion v1.4
• 🎬 RunwayML SD
• 🖼️ Automatic1111 WebUI

---

⚙️ Compilers & Low-Level Frameworks

---

🔧 TVM - Apache 🎯 End-to-end deep learning compiler stack Resources: TVM GitHub 🔨 LLVM ⚙️ Compiler infrastructure project Resources: LLVM Project ⚡ XNNPack - Google 🚀 High-efficiency floating-point neural network operators Resources: XNNPack GitHub

🔷 ARM-NN 💪 Inference engine for ARM platforms Resources: ARM-NN GitHub ARM-NN Tutorial 🧠 CMSIS-NN 📱 Efficient neural network kernels for ARM Cortex-M Resources: CMSIS-NN GitHub 📱 Samsung ONE 🔧 On-device Neural Engine compiler Resources: ONE GitHub

---

💼 Industry & Commercial Solutions

---

🚀 Deeplite

🎯 AI-Driven Optimizer for Deep Neural Networks

Focus:

⚡ Faster Inference

📦 Smaller Models

🔋 Energy Efficient

☁️ Cloud to Edge

🎯 Maintain Accuracy

🔗 Resources:

• Deeplite Website

---

🔧 Utility Frameworks & Tools

---

👁️ OpenCV 📷 Computer vision library with C++ support Resources: 📺 OpenCV C++ Playlist 🔨 Build OpenCV C++

🎬 VQRF - Video Compression 📹 Vector Quantized Radiance Fields Resources: VQRF GitHub

---

🖼️ Additional Model Architectures

---

🎯 PP-PicoDet 📱 Lightweight real-time object detector for mobile Resources: PP-PicoDet Paper

🔬 EtinyNet 🎯 Extremely tiny network for TinyML Resources: EtinyNet GitHub

---

🧠 Computing Architectures & APIs

---

Mobile & Embedded

Open-Source ISA

NVIDIA GPU

Apple GPU

Cross- Platform

Graphics & Compute

---

📚 Research Papers & Academic Resources

---

📖 Foundational Surveys (2024-2025)

🔍 Click to expand research papers

🌐 Edge Computing & Deep Learning

• 📄 Deep Learning With Edge Computing: A Review
• 📄 Convergence of Edge Computing and Deep Learning
• 📄 Machine Learning at the Network Edge
• 📄 Edge Deep Learning in CV & Medical Diagnostics

🔬 TinyML Specific

• 📄 From Tiny ML to Tiny DL: A Survey (2024)
• 📄 EtinyNet: Extremely Tiny Network
• 📄 Ultra-low Power TinyML System

⚡ State Space Models & Efficient Architectures

• 📄 Mamba: Linear-Time Sequence Modeling
• 📄 Mamba-360: Survey of SSMs
• 📄 eMamba: Efficient Edge Acceleration

👁️ Vision Models

• 📄 MobileNetV4 (ECCV 2024)
• 📄 ViT for Mobile/Edge Devices
• 📄 YOLO Evolution: v5 to YOLO26
• 📄 YOLOv10: Real-Time Detection

🔧 Model Compression & Optimization

• 📄 Model Compression for Carbon Efficient AI (2025)
• 📄 NAS Systematic Review (2024)
• 📄 Advances in Neural Architecture Search

📚 Collections

• ⭐ Awesome Embedded and Mobile Deep Learning

---

🎓 Contributing & Community

---

This repository serves as a comprehensive resource for AI edge computing and TinyML practitioners.

Contributions, updates, and corrections are welcome! 🚀

---

📊 Repository Stats

---

🏷️ Keywords

TinyML • Edge AI • Embedded ML • Model Compression • Quantization • Neural Architecture Search • YOLO • MobileNet • Transformer • State Space Models • ONNX Runtime • TensorRT • Inference Optimization • MCU • IoT • Real-Time AI

---

📅 Last Updated

January 2025

---