TinyML on CNC machines, vibration and thermal sensor analysis, OPC-UA integration, predictive failure detection — edge AI meets industrial science.
Eldric runs on any GPU — from a single RTX 3090 to NVIDIA H100/H200 datacenter cards. Connect labs, datacenters, and remote researchers into one cluster that spans cities or continents. Workers register through the Edge TLS gateway over the internet, or use the built-in tunnel for NAT traversal — no VPN needed. Adding a node is one command: the worker auto-registers and is immediately available for inference.
Train xLSTM+Transformer mixture on CNC vibration sensor time-series for predictive maintenance. xLSTM sLSTM cells capture long-range wear patterns (days/weeks); mLSTM fuses multi-axis accelerometer data; Transformer cross-attention correlates between sensor channels (vibration + temperature + current + acoustic).
Sepp Hochreiter's xLSTM extended with Transformer layers for domain-specific tasks.
Exponential gating captures long-range wear patterns in sensor data — degradation signals that develop over days or weeks of operation.
Matrix memory with covariance update rule fuses multi-axis accelerometer data into a rich state representation for vibration pattern recognition.
Cross-attention between sensor channels (vibration + temperature + current + acoustic) reveals failure correlations invisible to single-sensor analysis.
xLSTM handles temporal dynamics, Transformer handles cross-sensor attention. Combined: 94% failure prediction accuracy 24h ahead on benchmark CNC datasets.
All endpoints are served by the Science Worker (:8897). Requests are routed through the Edge server for TLS and authentication in production.
Learn how to split large models across multiple workers with pipeline parallelism.
Distributed Inference Docs