Show HN: MicroSafe-RL – Deterministic 1.18µs safety layer for Edge AI

Deterministic Safety Layer for Reinforcement Learning on Edge Hardware Ultra-lightweight runtime protection for embedded AI systems MicroSafe-RL is a minimalistic C++ safety module designed to monitor, evaluate, and constrain AI-generated control signals in real time on embedded systems. It operates as a deterministic safety bridge between: AI policies (Reinforcement Learning / LLM-based control) Physical systems (actuators, motors, valves) The system reduces the risk of unsafe or unstable behavior using constant-time statistical validation and constraint logic. ⚙️ Key Properties Metric Value Latency (measured) ~1 µs (Cortex-M3, DWT counter) Memory footprint 24 bytes (no dynamic allocation) Time complexity O(1) per step Architecture Deterministic, statistical Benchmarks are hardware-dependent and provided for reference only. 🧠 Core Mechanism MicroSafe-RL computes a stability signature of incoming signals using lightweight statistical features: short-term dispersion (variance / MAD-like behavior) deviation from rolling baseline optional dynamic component (signal velocity) This produces a penalty term: penalty = κ × (instability + α × deviation + β × dynamics) The penalty is then used to: attenuate unsafe outputs enforce hard safety bounds reshape reinforcement learning rewards 🔧 Functional Components - Signal Monitoring Fixed-size buffer (no heap) Tracks recent system state - Stability Estimation Constant-time statistical evaluation Noise-tolerant deviation metrics - Output Constraint Layer Soft control (attenuation / scaling) Hard constraints (clipping / bounding) - RL Integration (Optional) Converts instability into negative reward Enables safer policy convergence 📊 Example Runtime Output [ STABLE ] AI: 1.31 | Safe: 1.31 | Reward: 1.00 [ STABLE ] AI: 1.53 | Safe: 1.50 | Clamp applied [ ALERT ] AI: 2.10 | Safe: 1.42 | Attenuated Measured using on-chip cycle counter (DWT). 📂 Repository Structure . ├── MicroSafeRL.h ├── SafetyBridge.h ├── tools/ │ └── microsafe_profiler.py ├── examples/ │ ├── MicroSafe_Demo.ino │ └── advanced/ │ └── real_gemma_integration.py └── wrappers/ └── microsafe_gym.py 🛠️ Example Usage #include "MicroSafeRL.h" MicroSafeRL safety; void loop() { float ai_action = get_ai_output(); float sensor = read_sensor(); float safe_action = safety.apply_safe_control(ai_action, sensor); actuator.write(safe_action); } This project is designed for: research prototyping experimentation 🔬 Academic Status Preprint available via Zenodo Under peer review No validated safety guarantees are claimed at this stage. 📬 Licensing MicroSafe-RL is released under the MIT License for research and prototyping use. For production deployment in safety-critical or regulated environments, commercial licensing and support agreements are available. 📩 Contact: [email protected] (Commercial Licensing & Partnerships)