Jacob Beningo is an independent consultant and lecturer who specializes in the design of embedded software for resource constrained and low energy mobile devices. He has successfully completed projects across a number of industries including automotive, defense, medical and space. He enjoys developing and teaching real-time and reusable software development techniques using the latest methods and tools. He blogs for DesignNews.com about embedded system design techniques and challenges. Jacob holds Bachelor's degrees in Electrical Engineering, Physics and Mathematics from Central Michigan University and a Master's degree in Space Systems Engineering from the University of Michigan.
Running DSP Algorithms on Arm® Cortex®-M ProcessorsAvailable in 11 days, 22 hours and 43 minutes
The modern embedded system has many applications for digital signal processing (DSP), especially with the demand for intelligence on the end device and machine learning. DSP is an important tool for real-time embedded system engineers, critical for projects that require converting analog filter circuits into digital IIR or FIR filters, using Fast Fourier Transform (FFT) to observe a signal’s frequency component and even speech processing.
In this session, we will examine how attendees can speed development time with DSP software available through the Arm ecosystem, making DSP achievable in their own applications running on Arm Cortex-M based devices.
This session will include:
- How to use the free CMSIS-DSP library
- Examples for implementing FIR and IIR filters
- Using FFT to observe a signal’s frequency component
- Modern examples on how DSP is being applied to embedded systems
- Tool and techniques available to developers to speed up DSP implementations
Training and Deploying ML models to STM32 MicrocontrollersAvailable in 13 days, 19 hours and 43 minutes
Machine learning (ML) has often been considered a technology that operates on high-end servers and doesn’t have a place in traditional embedded systems. That perception is quickly changing. This workshop will explore how embedded software engineers can get started with machine learning for microcontroller based systems.
This session balances theory with practical hands-on experience using an STM32 development board.
Attendees will learn:
- How to collect and classify data
- Methods available to embedded developers to train a model
- Hands-on experience training a model
- How to convert a model to run on an STM32 MCU
- How to run an inference on a microcontroller
Additional details for development board and tools will be provided closer to the conference.