Embedded System Design Optimization and Adaptation using Compact System-Level Models

Maryland DSPCAD Research Group
Project Webpage


In this project, referred to as the Compact System-level Models (CSM) Project, we are developing new techniques to help advanced computing systems for signal processing better adapt to the environments in which they operate. This project is important because signal processing is everywhere (cell phones, computer networks, manufacturing systems, agriculture, etc.). Adapting to the environment helps these systems to operate more reliably by, for example, adapting to changing radio interference or the challenging radio environments presented by clusters of tall buildings. Many of these communication systems are also battery-operated or must run on limited energy; adapting to their operating environments helps to reduce their energy consumption and improve battery life. These techniques are particularly useful for cognitive radio, an emerging technology that allows devices for wireless communication (such as cell phones) to more efficiently use radio spectrum.

This project is developing new methods for creating software that can be reconfigured at run time. Typical software is created to operate in a particular mode; changing the software's operating conditions requires redesigning the software itself. New mathematical models and algorithms will allow system designers to create software that is designed to adapt itself dynamically to its environment. The project is addressing both models specifying the behavior of the software and for translating that specification into an efficient implementation.

The project is a collaboration among researchers at Georgia Institute of Technology, USA; Institut National des Sciences Appliquées (INSA) de Rennes, France; National Chiao Tung University, Taiwan; and University of Maryland at College Park, USA. The collaborators in Georgia, France, Taiwan, and Maryland provide complementary expertise in areas that include cyber-physical systems, cognitive radio algorithms, model-based design, and embedded signal processing. The collaboration also provides valuable international research experience for the Ph.D. student researchers involved in the project.




The project has produced a number of educational resources, including the following.

  • We published two educational videos on YouTube:
  • We delivered a Tutorial on Design for Low-Power Internet-of-Things (IoT) Systems at the 2018 International Symposium on Circuits and Systems in Florence Italy. The tutorial was delivered jointly with Prof. Francesca Palumbo of Università degli Studi di Sassari, Italy, and Prof. Jarmo Takala of Tampere University of Technology, Finland. The tutorial consisted of four modules. The slides for these modules are available here:

    GEMBench is a a benchmarking tool for evaluating implementations of solvers for Markov Decision Processes (MDPs). GEMBench stands for the Gpu-accelerated Embedded Mdp testBench. GEMBench is targeted to a specific embedded GPU platform, the NVIDIA Jetson platform, and is designed for future retargetability to other platforms. GEMBench is a novel open source software package that is intended to run on the target platform. The package contains libraries of MDP solvers, parsers, datasets and reference solutions, which provide a comprehensive infrastructure for understanding trade-offs among existing embedded MDP techniques, and experimenting with novel techniques. GEMBench can be downloaded from the following link: GEMBench Package Download. More details about GEMBench can be found in the following publication:

    A. Sapio, R. Tatiefo, S. Bhattacharyya, and M. Wolf. GEMBench: A platform for collaborative development of gpu accelerated embedded Markov decision systems. In Proceedings of the International Conference on Embedded Computer Systems: Architectures, Modeling, and Simulation, Samos, Greece, July 2019. To appear.


    A list of publications from the CSM project can be found on the CSM Project Publications Page.


    This research is supported in part by the Computer and Network Systems Program of the U.S. National Science Foundation under Grant No. CNS1514425 (University of Maryland), and CNS1513404 (Georgia Institute of Technology).


    This webpage was last updated on 05/21/2019.