TinyML brings intelligence to embedded devices, such as low-cost and energy-constrained microprocessors. Compression techniques, such as architecture search, pruning, and quantization, are used to accelerate model inference. In this talk, we begin with large-scale, high-fidelity non-linear models based on mathematical or physical formulations. We then train Reduced Order Models (ROM) using input-output data from the original first-principles model and test the generated C/C++ code before deploying it to embedded targets. We demonstrate the effectiveness of this approach using State of Charge (SoC) estimation for battery management onboard virtual vehicles as an example. This workflow captures the dynamics of the complex system while reducing the computational time required for simulations and real-time applications.
Schedule
Timezone: PDT
A TinyML Approach to Deploy Reduced-Order Model of Complex Systems on Microprocessor
Brenda ZHUANG, Engineering Manager
MathWorks
Gregory COPPENRATH, Product Marketing Manager
Mathworks
Brenda ZHUANG, Engineering Manager
MathWorks
Dr. Brenda Zhuang is a consulting engineer and engineering manager at MathWorks, where she leads a team responsible for software tools for automatic deployment of embedded applications, such as motor controls and deep learning, to microprocessors and FPGAs. Brenda joined MathWorks in 2007. She received her PhD from Boston University in Systems Engineering. She serves on the technical program committee in control theory, modeling and simulation.
Gregory COPPENRATH, Product Marketing Manager
Mathworks
Greg is the product marketing manager for Fixed-Point Designer and Deep Learning Toolbox Model Quantization Library. He has experience in the development of embedded systems and product development in the semiconductor industry. He received an MBA from Worcester Polytechnic Institute, an M.S. in Electrical Engineering from the University of Massachusetts Lowell, and received a B.S. in Electrical Engineering from WPI.
Schedule subject to change without notice.