Embedded AI for Signal Processing on the Edge: Streamlining Your Workflow with Agentic AI
Overview
Modern deep neural networks play a key role in advancing signal processing technologies across many industries and applications.
One of the key challenges for the practical use of AI on edge devices are the requirements for large computational resources, like high-performance processors or GPUs. As the quality and functionality of AI models improve, engineers need to fit increasingly complex models into system designs, while also meeting hard constraints on computational bandwidth and power consumption.
Implementing AI models onto edge devices and low-power processors often requires compressing them before turning them into embedded software. That may include simplifying their structure and adopting low-precision data types to reduce runtime memory and inference times, while retaining high accuracy.
This process, from data to deployment, involves knowledge-intensive steps. Agentic AI tools assist engineers at every stage, accelerating the overall development cycle and helping teams move from concept to deployment faster.
In this webinar, we use MATLAB to follow the main steps involved in taking an AI model from the training data to a lightweight embedded implementation. Using smart acoustic sensing as an example application, you learn how to:
- Identify AI models for signals, either provided with MATLAB or imported from Python frameworks like PyTorch
- Model signal processing and analysis systems including deep networks and DSP algorithms
- Simulate AI-based designs using live signals using MATLAB Apps and Simulink models
- Compress deep learning models, and quantize data types
- Generate embeddable C++ from system design including DSP and deep learning
- Optimize code for specific embedded devices, such as ARM Cortex and Qualcomm Hexagon
- Boost overall engineering productivity by using Agentic AI tools
About the Presenter
Dr. Ying Chen
Dr. Ying Chen is a Principal Application Engineer at MathWorks, specialising in signal processing, wireless system design, and transceiver development. She has experience across both academic and industrial research environments, with a particular focus on satellite communications and radar signal processing. Her work spans mission analysis, system‑level design, algorithm development, and hardware implementation. Ying has contributed to a range of industrial prototyping and field‑tested projects, including satellite communication systems and both passive and active radar systems. In her role at MathWorks, she works closely with customers to develop and deploy wireless systems, streamlining workflows and accelerating the transition from design concepts to operational capability.
Shine Rezaei Boroujeni
Shine Rezaei is a Senior Application Engineer at MathWorks with a background in machine learning and the Theory of Constraints (TOC). Over the past seven years, Shine worked as a Data Analyst at gold mining companies, contributing to a broad range of data-driven initiatives in both operational and technical domains. Shine holds an MPhil in Data Science, an MSc in Electrical and Computer Science, and a BSc in Biomedical Engineering.
