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.
Ahead-of-time compiler that fits ML models on embedded devices with hard memory budgets.
50.7x latency-optimized heterogeneous Vision Transformer (DeiT) hardware accelerator built on the Xilinx ZCU104 FPGA using DPU + custom HLS IPs.
Python ML for training a custom on-device cry model (knowledge-distilled from YAMNet, INT8, deployed on ESP32-S3)
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.
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.
AIoT motion‑state detection pipeline: a simulated ESP32 + MPU6050 sensor (Velxio) streams accelerometer data over MQTT to Node‑RED, a TinyML model trained in Edge Impulse classifies Idle/Move/Shake on the edge, and predictions plus Shake alarms are visualized live on a ThingsBoard dashboard.
ESP32 camera that escalates from gentle reminders to airhorn if you slouch
Hardware-aware face detection on Samsung GT-S7392 (ARM Cortex-A9)
Generative horror adventure with a quantized transformer running locally in a Game Boy-compatible ROM.
Static INT8 AOT compiler that turns fixed PyTorch MCU models into standalone C11
Curated Edge AI resources for computer vision & audio: hardware, frameworks, benchmarks, literature, and communities (excluding mobile).
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.
Notes and resources from Qualcomm On-device AI course, provided by DeepLearningAI
Compress PyTorch models for edge devices — CPU-only, no GPU, no retraining. One function call.
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.
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