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Legacy page

This page describes legacy neuralSPOT. For current tools and documentation, see heliaPROFILER, heliaPROFILER profile command, heliaPROFILER power measurement, heliaPROFILER PMU counters, heliaPROFILER supported boards, heliaPROFILER analyze, heliaAOT, heliaEstimator.

Tools Directory

Below is an overview of the scripts available in this directory and a brief description of their purpose:

Script Description
ns_autodeploy.py Deploys TFLite models to an EVB including binary creation, profiling, power measurement, and library generation.
ns_tflite_analyze.py Analyzes TFLite models to estimate MAC counts, memory reads/writes, and layer statistics; outputs reports in CSV/Excel.
ns_ad_batch.py Batch deployment of multiple models using YAML configuration files.
ns_test.py Automated testing framework for neuralSPOT using configuration files and command-line arguments.

Overview

This directory contains a collection of utility scripts that support various tasks for the neuralSPOT project. These tools facilitate data communication between an EVB (Evaluation Board) and a PC, model analysis and deployment, automated testing, power measurement, and live data visualization. Below is an overview of each script and instructions on how to use them.

Contents

  • ns_autodeploy.py The primary tool for deploying TFLite models to an EVB board. This script supports:
  • Creating validation binaries.
  • Profiling model performance.
  • Power measurement (via Joulescope or onboard performance capture).

  • Generating minimal static libraries and AmbiqSuite examples. See the autodeploy Theory of Operations for more details.

  • ns_tflite_analyze.py Analyzes TensorFlow Lite (TFLite) model files find optimization targets, estimate MAC counts, memory reads/writes, and other layer statistics. It generates human-readable output in table format, as well as CSV and Excel reports.

  • ns_test.py Runs automated tests for the neuralSPOT project. It uses a configuration file (INI format) along with command-line parameters parsed via pydantic_argparse to generate and run tests.

  • ns_ad_batch.py Batch deploys multiple neural network models defined in a YAML configuration file. For each model, it builds the appropriate ns_autodeploy command and executes it with support for retries and logging. See the ad_batch Usage Guide for more details

Prerequisites

ns_autodeploy can be installed using pip: 

cd .../neuralSPOT
python -m pip install "pip @ https://files.pythonhosted.org/packages/3f/06/3e4420302363fbb71be3c7f35ce042f54bfca330e3e66b3a2e3c4cca579f/pip-26.2.2-py3-none-any.whl#sha256=fd7f4dfa8190e014013b9f08f3e973f4e4c26ffd179a204895533a7254813a70"  # only if pip --version is older than 26.2.2
pip install .

To run the other tools, ensure that you have Python 3.6 or later installed along with the following packages:

  • Core Packages: numpy, pyyaml, pydantic, pydantic_argparse

  • Audio & Signal Processing: soundfile, pyogg

  • Communication & RPC: erpc

  • Data Packing & Messaging: msgpack, msgpack_numpy

  • Visualization: dash, plotly

You can install the required packages using pip. For example:

```bash pip install numpy pyyaml pydantic pydantic_argparse soundfile pyogg erpc msgpack msgpack_numpy dash plotly