Dynamically Adaptable Reconfigurable Embedded Systems (DARES)
Significant increases in application complexity demand processing requirements that exceed the performance achievable by current processors using software only implementations. For example, recent multimedia standards, such as JPEG2000, have significantly increased computational demands compared to previous standards. In an effort to alleviate the cost of developing software and/or hardware solutions capable of fully supporting such standards, many define several profiles – specific settings for various configurable parameters – that reduce the level of complexity needed to implement a specific profile. However, the number and variability of profiles even within a single domain still precludes traditional hardware implementations as a viable option for most applications.
The Data-Adaptable Reconfigurable Embedded Systems (DARES) project focuses on developing hardware/software codesign and reconfigurable computing methodologies driven by data-adaptability. This data-adaptable approach allows designers to directly model data configurability of an application, thereby enabling a solution that can be dynamically reconfigured at runtime based on the profile of incoming data. The DARES project combines modeling techniques for capturing the data configuration space with new hardware/software codesign techniques to synthesize reconfigurable circuits and communication resources directly from the data/application model. The resulting hardware/software implementation provides the flexibility of software with the performance of hardware.
- Overview DARES Modeling Approach: DARES Overview
Publications, Presentations, and Tutorials
- N. Sandoval, C. Mackin, S. Whitsitt, R. Lysecky, J. Sprinkle. Runtime Hardware/Software Task Transition Scheduling for Runtime-Adaptable Embedded Systems. International Conference on Field-Programmable Technology (ICFPT), Accepted for Publication, 2013.
- R. Lysecky, N. Sandoval, S. Whitsitt, C. Mackin, J. Sprinkle. Efficient Reconfiguration Methods to Enable Rapid Deployment of Runtime Reconfigurable Systems. Asilomar Conference on Signals, Systems and Computers, Accepted for Publication, 2013.
- How You Can Learn To Stop Worrying and Love Reconfigurable Embedded Systems: A Tutorial. IEEE International Conference and Workshops on the Engineering of Computer Based Systems (ECBS), Phoenix, AZ, 2013.
- N. Sandoval, C. Mackin, S. Whitsitt, R. Lysecky, J. Sprinkle. System Throughput Optimization and Runtime Communication Middleware Supporting Dynamic Software-Hardware Task Migration in Data Adaptable Embedded Systems. IEEE International Conference on Engineering of Computer-Based Systems (ECBS), 2013.
- S. Whitsitt, J. Sprinkle, R. Lysecky. An Overseer Control Methodology for Data Adaptable Embedded Systems. International Workshop on Multi-Paradigm Modeling (MPM), 2012.
- A. Milakovich, V. S. Gopinath, R. Lysecky, J. Sprinkle. Automated Software Generation and Hardware Coprocessor Synthesis for Data-Adaptable Reconfigurable Systems IEEE International Conference on Engineering of Computer-Based Systems (ECBS), 2012.
- V. S. Gopinath, J. Sprinkle, R. Lysecky. Modeling of Data Adaptable Reconfigurable Embedded Systems. IEEE Workshop on Model-Based Development for Computer Based Systems, pp. 276-283, 2011. PDF
- S. Mahadevan, V. S. Gopinath, R. Lysecky, J. Sprinkle, J. Rozenblit, M. W. Marcellin. Hardware/Software Communication Middleware for Data Adaptable Embedded Systems. IEEE International Conference on Engineering of Computer-Based Systems (ECBS), pp. 34-43, 2011.
- A. Milakovich, V. Gopinath, R. Lysecky, J. Sprinkle. Automated Software Generation and Hardware Coprocessor Synthesis for Data-Adaptable Reconfigurable Systems. Workshop on Adaptive and Reconfigurable Embedded Systems (APRES), 2011.
- Roman Lysecky (PI), Associate Professor
- Jonathan Sprinkle (Co-PI), Assistant Professor
- Jerzy Rozenblit (Co-PI), Professor
- Michael Marcellin (Co-PI), Professor
- Sean Whitsitt, Graduate Research Assistant
- Nathan Sandoval, Graduate Research Assistant
- Casey Mackin, Undergraduate Research Assistant
- David Shcwartz, Undergraduate Research Assistant
- Vineet Dixit, Undergraduate Research Assistant
- Vijay Gopinath, Graduate Research Assistant (MS ECE 2011, now Microsoft)
- Kyle Merry, Undergraduate Research Assistant (BS 2011, now with Sandia)
- Andrew Milakovich, Undergraduate Research Assistant (BS 2011, now with Proctor & Gamble)
This research was supported by the National Science Foundation (CNS-0915010).