AM263Px MCU+ SDK: Generic Timeseries Regression

| AM263Px MCU+ SDK 26.00.00 |

Generic Timeseries Regression

# Introduction

Generic Timeseries Regression is a hello world example for understanding usage of regression AI models on TI MCU. This example uses a simple synthetic regression dataset where the target variable y = 1.2 sin(x) + 3.2 cos(x), with x randomly generated in the range [0, 3]. This serves as a fundamental example for deploying regression models on AM26x microcontrollers.

# Supported Combinations

Parameter | Value
--- | ---
CPU + OS | r5fss0-0 nortos
Toolchain | ti-arm-clang
Board | am263px-lp
Example folder | examples/ai/generic_timeseries_regression/

# Dataset and Model Details

## Dataset

A synthetic regression dataset where:

- x is randomly generated in the range [0, 3]
- Target variable y = 1.2 sin(x) + 3.2 cos(x)

Column Name | Description
--- | ---
x | Randomly generated input in range [0,3]
y | Target: y = 1.2 sin(x) + 3.2 cos(x)

## Model Architecture

This lightweight regression model contains approximately 1,800 parameters with one input batch normalization layer, 4 convolution layers, and 2 fully connected layers.

## Input Features

The model takes 4D input (N,C,H,W):

- N (1): batch size which is restricted to 1
- C (1): channels which is 1 for signal
- H (10): samples of timeseries signals which is 10 in this example
- W (1): width of samples is restricted to 1 for timeseries applications

## Output

This model produces a 1D output representing the predicted values of target variable y.

# Feature Extraction Configuration

For this regression task, a SimpleWindow transformation is used, which makes use of the previous frame_size datapoints for prediction of the current target value. A frame size of 10 is used in this example.

No FFT or frequency-domain feature extraction is applied - the model learns directly from raw signal values.

# Steps to Run the Example

- When using CCS projects to build, import the CCS project for the required combination and build it using the CCS project menu (see Using SDK with CCS Projects).
- When using makefiles to build, note the required combination and build using make command (see Using SDK with Makefiles)
- Launch a CCS debug session and run the executable, see CCS Launch, Load and Run
- The application will run inference on test vectors and compare against expected outputs

# See Also

AI Examples

# Sample Output

Generic Timeseries Regression Example Started ...
All tests have passed!!
Golden vectors matched: 3 not matched: 0