AutoML for MCU: How Luviner Automatically Finds the Best Model for Your Chip
Specify your microcontroller. Set your memory budget. Luviner tests multiple architectures and delivers the one that maximizes accuracy — automatically.
The Memory Problem
When deploying AI on microcontrollers, you face a hard constraint that doesn’t exist in cloud ML: fixed memory. A Cortex-M0 might have 32 KB of flash and 8 KB of RAM. An ESP32 gives you 4 MB of flash but only 520 KB of RAM. Every byte counts.
The traditional approach is trial and error: train a model, check if it fits, adjust parameters, repeat. This wastes hours and requires deep knowledge of both ML and embedded systems.
Let the Platform Do the Work
Luviner’s AutoML for MCU automates this entirely:
- Select your target MCU — choose from 12+ pre-configured hardware profiles (Cortex-M0, M3, M4, M7, ESP32, ESP32-S3, RISC-V, nRF52) or specify custom Flash/RAM limits
- Upload your data — standard CSV with sensor readings and labels
- Luviner searches — multiple candidate architectures are evaluated against your hardware constraints. Only architectures that fit your memory budget are considered
- Best model delivered — the winning architecture is fully trained and exported as pure C, ready to flash
Why This Matters
Without AutoML, you’re guessing. Too many neurons and your model doesn’t fit. Too few and accuracy suffers. The sweet spot depends on your data, your chip, and how the architecture interacts with your specific classification task.
AutoML eliminates this guesswork. It systematically explores diverse architectures — not just the largest ones that fit, but creative combinations that might achieve better accuracy with less memory.
Pre-Configured Hardware Profiles
Luviner ships with accurate memory profiles for the most common MCU families:
- ARM Cortex-M0/M0+ — ultra-low-cost, tight memory constraints
- ARM Cortex-M3 — legacy industrial devices
- ARM Cortex-M4 — mainstream IoT
- ARM Cortex-M7 — high-end embedded
- ESP32 / ESP32-S3 — WiFi/BLE capable, generous flash
- RISC-V — emerging open-standard architectures
- Nordic nRF52 — Bluetooth Low Energy wearables
Each profile includes accurate Flash and RAM estimates so you know before training whether the model will fit.
No Competitor Offers This
Edge Impulse requires you to manually configure your model architecture. TensorFlow Lite Micro leaves architecture search entirely to the user. STM32Cube.AI works only with STMicroelectronics chips. Luviner is the only platform that offers automatic architecture search across all major MCU families.
Get Started
Upload your CSV, select your target chip, and let AutoML find the optimal model. It’s that simple.