Machine Learning Systems

TinyML & Edge AI: On-device inference, model quantization, embedded ML, ultra-low-power AI for microcontrollers and IoT devices.

Curated Edge AI resources for computer vision & audio: hardware, frameworks, benchmarks, literature, and communities (excluding mobile).

ESP32 camera that escalates from gentle reminders to airhorn if you slouch

Notes and resources from Qualcomm On-device AI course, provided by DeepLearningAI

This is open source library for creating artificial neural network in c programming language for general purpose use.

CS2 Skin Preview & Customization Utility for Weapons and Inventory is a visual tool for exploring and customizing weapon and inventory appearances in Counter-Strike 2, designed for previews, loadout styling, and cosmetic experimentation.

Estudo comparativo de arquiteturas de deep learning (CNN 1D, MLP, GRU, LSTM) para predição de temperatura em sistemas TinyML. Análise de performance, precisão e viabilidade para deploy em RP2040 com fusão de sensores AHT20/BMP280. Horizontes de 5, 10 e 15 minutos.

Fajar Lang (fj) — Systems programming language for embedded ML & OS development. Compiler-enforced safety with @kernel/@device/@safe contexts. Rust-based compiler with Cranelift/LLVM backends. Made in Indonesia.

Real-time motor speed classification using TinyML on Raspberry Pi Pico W. MLP neural network trained with TensorFlow deployed on embedded hardware (5.3 KB model). Classifies motor vibration into 4 speed levels using MPU6050 accelerometer with live OLED display feedback. Complete ML workflow from data collection to edge deployment.

Deploy and manage ML models at the edge — OPC-UA integration, PLC connectivity, real-time inference on embedded hardware for sub-millisecond decisions

Multiposition heart sound analysis

Don't Think It Twice: Exploit Shift Invariance for Efficient Online Streaming Inference of CNNs

Hardware-aware face detection on Samsung GT-S7392 (ARM Cortex-A9)

Static ONNX graph repair tool that zero pads weight tensors to satisfy CMSIS-NN fast path alignment constraints, no retraining required.