Robert Barnes Utah State University Department of Electrical and Computer Engineering Thesis Defense, November 13 th 2008

 Introduction & Background  System Design  Results & Conclusions

 Increasing Demands for Spacecraft  Low Power  Fault Tolerant  Flexibility  High Performance  Solution: FPGA

 Flexible Extended Kalman Filter (EKF) System on an FPGA  Adaptable to changing performance requirements (scalable).  System adaptable to other algorithms (DWT).  Outperform RAD750 PowerPC  Explore applications of dynamic reconfiguration.

 To navigate in space an autonomous spacecraft must accurately estimate its state from noisy measurements.  The filter is very flexible  Estimate a system’s state from only a single sensor  Estimate the bias in sensors  Determine an unknown system model  Predict a future states

 A network of simple processing elements (PE) which rhythmically process and pass data to nearest neighbours to process larger complex tasks.  Features:  Modularity  Regularity  Locality  Synchronous  Pipelined  Data Reuse

Figure Source: Jeff Carver

 JBits  Interface to make changes to the Bitstream  Modular Design Flow  Early Access Design Flow  Improved Modular Design Flow

 Soft scaling  Using conditional variable loops and conditional statements, software can easily be made to scale to different parameters.  Static Hardware Scaling  Using MUXes a hardware architecture can be designed where data can be re-routed to different hardware cores.  Reconfigurable Hardware Scaling  Using partial dynamic reconfiguration the physical size of the systolic array can be scaled.

 Introduction & Background  System Design  Results & Conclusions

PolySAF Co-Processor

 Introduction & Background  System Design  Results & Conclusions

 A polymorphic systolic array framework (PolySAF).  Programmable switchboxes and protocol to allow dynamic scaling in the array.  Efficient EKF and DWT accelerators  Speedup of at least 4.18x and 6.61x over PowerPC for EKF and DWT.  Integration of bitstream relocation and bitstream compression into a practical system.  2.7x improvement in reconfiguration time.  A 44% improvement in BRAM usage.  The flexible and simple framework allows this design to host a broad range of algorithms.  Dynamic reconfiguration is powerful, but it is not useful in every application. The trade-offs must be weighed carefully.

 R. Barnes and A. Dasu, “Hardware/software Co- designed Extended Kalman Flter on an FPGA,” in The International Conference on Engineering of Reconfigurable Systems and Algorithms (ERSA),  R. Barnes, A. Dasu, J. Carver, and R. Kallam, “Dynamically Reconfigurable Systolic Array Accelerators: A case study with EKF and DWT Algorithms,” Institution of Engineering and Technology (IET) Computers & Digital Techniques. In Review.

 Hours: 4.33wks/month*16m onths*(>40hours/wk) = ~2771hours  Embedded C: ~6,000  Verilog Code: ~3,222  Python: ~1015  Tools:  EDK  ISE  Modelsim  MatLab  Xpower  PlanAhead  Eclipse  Simics  Python