1.2. System Architecture

This page describes the architecture of Tiny ML Tensorlab and how its components work together to provide an end-to-end ML workflow.

1.2.1. High-Level Architecture

┌─────────────────────────────────────────────────────────────────┐
│                        USER INPUT                               │
│                    (YAML Config File)                           │
└─────────────────────────────────────────────────────────────────┘
                               │
                               ▼
┌─────────────────────────────────────────────────────────────────┐
│                     tinyml-modelzoo                             │
│  • Entry point for users                                        │
│  • Example configurations                                       │
│  • Model definitions                                            │
└─────────────────────────────────────────────────────────────────┘
                               │
                               ▼
┌─────────────────────────────────────────────────────────────────┐
│                    tinyml-modelmaker                            │
│  • Orchestrates the workflow                                    │
│  • Calls training, testing, compilation                         │
└─────────────────────────────────────────────────────────────────┘
                               │
           ┌───────────────────┼───────────────────┐
           ▼                   ▼                   ▼
┌──────────────────┐ ┌──────────────────┐ ┌──────────────────┐
│ tinyml-tinyverse │ │   tinyml-model   │ │  TI Neural       │
│  • Training      │ │   optimization   │ │  Network         │
│  • Feature ext.  │ │  • Quantization  │ │  Compiler        │
│  • Testing       │ │  • QAT/PTQ       │ │  • Device code   │
└──────────────────┘ └──────────────────┘ └──────────────────┘
                               │
                               ▼
┌─────────────────────────────────────────────────────────────────┐
│                      OUTPUT ARTIFACTS                           │
│  • mod.a (compiled model)                                       │
│  • tvmgen_default.h (API header)                                │
│  • test_vector.c (validation data)                              │
└─────────────────────────────────────────────────────────────────┘

1.2.2. Component Details

1.2.2.1. tinyml-modelzoo

Purpose: Customer-facing entry point containing models and examples.

Key Contents:

examples/ - Ready-to-run YAML configurations for various applications
tinyml_modelzoo/models/ - Neural network model definitions
tinyml_modelzoo/model_descriptions/ - Model metadata for GUI integration
tinyml_modelzoo/device_info/ - Device performance benchmarks

Model Organization:

tinyml_modelzoo/models/
├── classification.py      # Time series classification models
├── regression.py          # Time series regression models
├── anomalydetection.py    # Autoencoder models
├── forecasting.py         # Time series forecasting models
└── image.py               # Image classification models

1.2.2.2. tinyml-modelmaker

Purpose: Orchestrates the end-to-end workflow.

Key Functions:

Parse configuration files
Manage project directory structure
Call training scripts from tinyml-tinyverse
Invoke quantization from tinyml-modeloptimization
Run TI Neural Network Compiler

Output Directory Structure:

data/projects/<dataset_name>/run/<run_name>/
├── training/
│   ├── base/              # Float32 model training outputs
│   │   ├── best_model.pt
│   │   ├── training_log.csv
│   │   └── *.png (visualizations)
│   └── quantization/      # Quantized model outputs
│       ├── best_model_quantized.pt
│       ├── best_model.onnx
│       └── golden_vectors/
├── testing/               # Test results
└── compilation/           # Compiled artifacts
    └── artifacts/
        ├── mod.a
        └── tvmgen_default.h

1.2.2.3. tinyml-tinyverse

Purpose: Core training infrastructure and utilities.

Key Components:

references/ - Task-specific training scripts:
- timeseries_classification/train.py
- timeseries_regression/train.py
- timeseries_forecasting/train.py
- timeseries_anomalydetection/train.py
- image_classification/train.py
common/ - Shared utilities:
- datasets/ - Dataset loaders for various formats
- models/ - Base model classes and utilities
- transforms/ - Feature extraction transforms
- augmenters/ - Data augmentation functions
- compilation/ - TVM compilation interface

1.2.2.4. tinyml-modeloptimization

Purpose: Model quantization toolkit.

Key Features:

TINPU Wrappers - For NPU-enabled devices (F28P55, etc.)
Generic Wrappers - For non-NPU devices
QAT Support - Quantization-aware training
PTQ Support - Post-training quantization

1.2.3. Data Flow

Training Pipeline:

Raw Data → Data Loading → Augmentation → Feature Extraction → Model → Loss → Optimizer
                                                               ↑
                                                       (model.pt saved)

Inference Pipeline:

Raw Data → Feature Extraction → Quantized Model → Output Classes/Values
              ↑                       ↑
    (user_input_config.h)       (mod.a from compilation)

1.2.4. Configuration System

The entire workflow is driven by YAML configuration files:

common:
  target_module: 'timeseries'
  task_type: 'generic_timeseries_classification'
  target_device: 'F28P55'

dataset:
  dataset_name: 'my_dataset'
  input_data_path: '/path/to/data'

data_processing_feature_extraction:
  feature_extraction_name: 'Generic_1024Input_FFTBIN_64Feature_8Frame'

training:
  model_name: 'CLS_1k_NPU'
  batch_size: 256
  training_epochs: 20

compilation:
  enable: True

See Understanding Config for complete configuration reference.

1.2.5. Integration Points

External Tools:

TI Neural Network Compiler (NNC) - Compiles ONNX models to device code
Code Composer Studio (CCS) - IDE for device deployment
C2000Ware / MSPM0 SDK - Device-specific libraries

Data Formats:

Input: CSV, TXT, NPY, PKL
Model: PyTorch (.pt) → ONNX (.onnx)
Output: Static library (.a), C header (.h)