Embedded AI
Quantizing TinyML Neural Models for ESP32-S3 Edge Inference
Engineering on-device neural plant voice synthesis on low-power microcontrollers with volatile SRAM privacy.
S
Subhash Koli
Founder, Vriksh Vani
July 202611 min read
On-Device TinyML Requirements
Privacy and low latency require that no telemetry audio or thermal frame buffers leave the NIH-01 hardware hub. To achieve this, quantized neural models run using TensorFlow Lite for Microcontrollers.
Running on an ESP32-S3 dual-core microcontroller clocked at 240MHz, inference executes locally while maintaining low power consumption and total data privacy.