Alex Haya
Backend engineering / AI infrastructure / Python

Case study / v0.2.0-beta.1

TinyML Deployer

From original graph to checked ESP32-S3 execution.

Signal to Silicon

36 seconds / Sound on

Follow a signal through the model graph, INT8 weights, generated C++ and measured prediction differences to the ESP32-S3 target. The board photograph is a hardware reference; physical-board testing remains pending.

HARDWARE REFERENCE / ESP32-S3

Original graph.
Explicit limits.
Traceable output.

Espressif ESP32-S3-DevKitC-1 development board, official manufacturer reference image.
QEMU verification: archived evidencePhysical-board validation pending

Inspect the observations.

Records Table / sortable, filterable, traceable

The fixtures use different model graphs. Inspect each independently. Recorded latency describes emulated time and cannot establish physical-board performance.

Integration fixture
Verifying the archived reports before displaying observations.
Source record
Loading archived data.Download selected JSON report ↗

Run the workflow.

Code Block / tagged source

Python 3.11 or 3.12. Install this version from source; the older published 0.1 PyPI package uses a different interface.

INSTALL / LINUX, WSL OR macOS
git clone --branch v0.2.0-beta.1 --depth 1 \
  https://github.com/alexhaya4/tinyml-deployer.git
cd tinyml-deployer
python -m venv .venv
. .venv/bin/activate
python -m pip install -e '.[emulation]'
tinyml-deployer --help

Watch the archived 54-second CLI recording

This is the previous captioned, silent replay of recorded command output. It is included as source material, not presented as a new hardware film. The sine conversion and separate compiled firmware fixtures are identified in the transcript.

Complete recording transcript ↗

Source observation

Model SHA-256
Dataset SHA-256
Original CI run ↗