Validate Code with Processor-in-the-Loop
Run generated code on Raspberry Pi hardware and compare its outputs with MATLAB results to make sure the algorithms work as expected and meet performance requirements.
For more information on the product stack required for MATLAB PIL simulation using Raspberry Pi Blockset, see Product Stack for Raspberry Pi Blockset.
Topics
- Product Stack for Raspberry Pi Blockset
Identify minimum required product(s) for Raspberry Pi Blockset to best suit your end goals.
Related Information
Featured Examples
SIL and PIL Verification for Deployment on Raspberry Pi
Verify the generated code by using the software-in-the-loop (SIL) and processor-in-the-loop (PIL) executions
Keyword Spotting in Noise Code Generation on Raspberry Pi
Demonstrates code generation for keyword spotting using a Bidirectional Long Short-Term Memory (BiLSTM) network and mel frequency cepstral coefficient (MFCC) feature extraction on Raspberry Pi®. MATLAB® Coder™ with Deep Learning Support enables the generation of a standalone executable (.elf) file on Raspberry Pi. Communication between MATLAB (.mlx) file and the generated executable file occurs over asynchronous User Datagram Protocol (UDP). The incoming speech signal is displayed using a timescope. A mask is shown as a blue rectangle surrounding spotted instances of the keyword, YES. For more details on MFCC feature extraction and deep learning network training, visit Keyword Spotting in Noise Using MFCC and LSTM Networks.
Speech Command Recognition Code Generation on Raspberry Pi
Generate code and deploy feature extraction and speech command recognition network on Raspberry Pi hardware.
Acoustics-Based Machine Fault Recognition Code Generation on Raspberry Pi
Demonstrates code generation for acoustics-based machine fault recognition using a long short-term memory (LSTM) network and spectral descriptors. This example uses MATLAB® Coder™, MATLAB Coder Interface for Deep Learning, Raspberry Pi® Blockset to generate a standalone executable (.elf) file on a Raspberry Pi. The input data consists of acoustics time-series recordings from faulty or healthy air compressors and the output is the state of the mechanical machine predicted by the LSTM network. This standalone executable on Raspberry Pi runs the streaming classifier on the input data received from MATLAB and sends the computed scores for each label to MATLAB. Interaction between MATLAB script and the executable on your Raspberry Pi is handled using the user datagram protocol (UDP). For more details on audio preprocessing and network training, see Acoustics-Based Machine Fault Recognition.
Detect and Track Object Using Deep Learning on Raspberry Pi
Use the Raspberry Pi® Blockset to deploy a deep learning algorithm that detects and tracks an object in Connected IO and PIL modes. This algorithm uses the ResNet-18-based YOLOv2 neural network to identify the object captured by the camera mounted on a servo motor and connected to the Raspberry Pi hardware. You can experiment with different objects in your surroundings to see how accurately the network detects images on the Raspberry Pi hardware.
Audio Event Classification Using TensorFlow Lite on Raspberry Pi
Demonstrates audio event classification using a pretrained deep neural network, YAMNet, from TensorFlow™ Lite library on Raspberry Pi®. You load the TensorFlow Lite model and predict the class for the given audio frame on Raspberry Pi using a processor-in-the-loop (PIL) workflow. To generate code on Raspberry Pi, you use Embedded Coder®, Raspberry Pi Blockset and Deep Learning Toolbox Interface for TensorFlow Lite. Refer to Audio Classification and yamnet classification for more details on the YAMNet model description.
