Atome LM started as one tiny ternary language model you could run on a $5 microcontroller. Atome LM v2 — codenamed SuperESP — turns that same 1.58-bit engine into a suite of on-device AI applications: instead of generating text, the chip now classifies the world around it, fully offline.
What v2 adds
- 12 on-device apps — 11 applied "heads" plus an on-device OS dispatcher: agriculture, voice commands, motion/gesture, sound events, machine anomaly, air quality, energy/NILM, occupancy, wearable activity, water-leak, predictive time-to-failure — each a tiny ternary classifier.
- Runs on the real chip: all 12 ran on a physical ESP32-WROOM-32 with ~27 KB of state and 265 KB free heap, every on-device decision matching the host bit-for-bit.
- Works on any ESP32: one installer auto-detects the chip (ESP32 / S2 / S3 / C3 / C6 / H2, Xtensa or RISC-V) and flashes the matching firmware — no toolchain to install.
- Make your own in minutes: a logger firmware records your sensor to CSV, then
train → report → flashbuilds a custom classifier. No ML expertise. - Trust built in: every model is Ed25519-signed and integrity-checked on load, with a tamper-evident log of decisions — auditable edge AI.
Honest about what it is
This is a real, production-grade open kit — not magic. Several demo heads ship on physics-grounded synthetic data (clearly labelled), and you replace them with your own real data through the same CSV path. Voice keyword-spotting works but is modest on a 20 KB ternary model. We publish the numbers, the tests, and the honest limits rather than a marketing benchmark.
Get it
The v1 engine and weights are already public on GitHub and Hugging Face under Apache-2.0. The v2 SuperESP kit ships the applied layer on top. If you'd rather have it done, certified, or licensed for your product, see services.
Frequently asked questions
What is Atome LM v2 (SuperESP)?
Atome LM v2, codenamed SuperESP, turns the tiny ternary Atome engine into a suite of on-device AI applications — 11 sensor classifiers plus an on-device dispatcher — that run fully offline on a $5 ESP32 instead of generating text.
Does it run on any ESP32?
Yes. One installer auto-detects the chip (ESP32, S2, S3, C3, C6, H2 — both Xtensa and RISC-V) and flashes the matching prebuilt firmware. The ~27 KB model state fits every mainline variant with large headroom.
Can I train my own classifier without machine-learning experience?
Yes. A logger firmware records your sensor readings to a CSV, then a single train command builds a bit-exact, signed model you flash to the board.