Insights on Edge AI, embedded ML, and tiny neural networks.
Tamper resistance, self-healing, multi-hop reach, intelligent sharing, and on-field learning — all running on 2 EUR microcontrollers. No existing product or research combines all five.
Read more →Each sensor has its own brain. They share neural states over a 24-byte mesh protocol. Together, they classify what no single node can — with zero cloud dependency.
Read more →Most predictive maintenance systems need examples of every failure mode. Luviner only needs your normal operation data — and detects 100% of anomalies.
Read more →Specify your microcontroller. Set your memory budget. Luviner tests multiple architectures and delivers the one that maximizes accuracy — automatically.
Read more →Luviner is the first platform to offer drift detection directly on the microcontroller. No cloud, no forward pass, just 24 bytes of extra RAM.
Read more →We benchmarked Luviner's streaming liquid neurons against standard approaches on industrial monitoring. Streaming achieved 86.8% accuracy, beating windowed (84.1%) and stateless (82.9%) — with zero buffers.
Read more →We tested Luviner on the UCI HAR public dataset — real smartphone sensor data, 6 activities, 30 volunteers. Full-sequence streaming neurons beat both stateless and windowed approaches on continuous activity monitoring.
Read more →We benchmarked Luviner's streaming streaming neurons against standard approaches on industrial monitoring. Streaming achieved 88.5% accuracy, beating windowed (83.8%) and stateless (83.7%) — with zero buffers.
Read more →A complete walkthrough of the Luviner workflow — from uploading raw sensor data to flashing a compiled AI binary on your microcontroller.
Read more →Traditional deep learning architectures waste resources on MCUs. Liquid Neural Networks offer 30x energy savings with comparable accuracy — here is why.
Read more →Traditional deep learning architectures waste resources on MCUs. Neural Networks offer 30x energy savings with comparable accuracy — here is why.
Read more →A deep dive into deploying a predictive maintenance model on a $4 microcontroller — no cloud, no GPU, no compromise.
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