Machine Learning Systems

MINERVA - Minimal Inference Engine for Robust, Verifiable, and Authenticated ML. Encrypted, integrity-verified neural network inference for MCUs down to ATmega328P.

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

Pure-Rust prompt-injection detector with 1.5MB embedded MLP classifier. 98.40% accuracy, p50 14ms CPU inference, bindings for Python/JS/Go. Apache-2.0/MIT alternative to Rebuff (archived) and Lakera Guard.

Python ML for training a custom on-device cry model (knowledge-distilled from YAMNet, INT8, deployed on ESP32-S3)

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.

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

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

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.

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

Novel DBSCAN-based FPGA system for automatic modulation classification and NDA SNR estimation. O(n²)→O(n) complexity reduction. 71.7% lower power than state-of-the-art.

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.

Compress PyTorch models for edge devices — CPU-only, no GPU, no retraining. One function call.

50.7x latency-optimized heterogeneous Vision Transformer (DeiT) hardware accelerator built on the Xilinx ZCU104 FPGA using DPU + custom HLS IPs.

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

SchoolPrint AI is a suite of edge-AI tools and a web dashboard that help schools cut water, food, and energy waste by detecting leaks, sorting compost, and visualizing consumption in one conclusive dashboard.

End-to-end TinyML pipeline: gesture recognition on Arduino Nano 33 BLE Sense — 1D CNN (97.6% acc, 26.9 KB INT8) + 5 ML baselines, BLE→WebSocket→web dashboard.

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.

Hands-on labs for ML engineers to deploy edge AI inference with ExecuTorch on Arm platforms using PyTorch, XNNPACK, and Ethos-U (educational)