Architecture-level fault-tolerance for biomedical implants
Paper i proceeding, 2012

In this paper, we describe the design and implementation of a new fault-tolerant RISC-processor architecture suitable for a design framework targeting biomedical implants. The design targets both soft and hard faults and is original in efficiently combining as well as enhancing classic fault-tolerance techniques. The proposed architecture allows run-time tradeoffs between performance and fault tolerance by means of instruction-level configurability. The system design is synthesized for UMC 90nm CMOS standard-process and is evaluated in terms of fault coverage, area, average power consumption, total energy consumption and performance for various duplication policies and test-sequence schedules. It is shown that area and power overheads of approximately 25% and 32%, respectively, are required to implement our techniques on the baseline processor. The major overheads of the proposed architecture are performance (up to 107%) and energy consumption (up to 157%). It is observed that the average power consumption is often reduced when a higher degree of fault tolerance is targeted. It is shown that test sequences can effectively be scheduled during the available program stalls and that nearly all soft faults are tolerated by using instruction duplication. The main advantages of the proposed architecture are the high portability of the introduced architecture-level fault-tolerance techniques, the flexibility in trading processor overheads for required fault-tolerance degree as well as affordable area and power consumption overheads.


R.M. Seepers

Delft University of Technology

Erasmus University Medical Center

C. Strydis

Delft University of Technology

Erasmus University Medical Center

Georgi Gaydadjiev

Chalmers, Data- och informationsteknik, Datorteknik

2012 International Conference on Embedded Computer Systems: Architectures, Modeling and Simulation, IC-SAMOS 2012; Samos; Greece; 16 July 2012 through 19 July 2012

104-112 6404163


Data- och informationsvetenskap