Scrubbing Optimization via Availability Prediction (SOAP) for Reconfigurable Space Computing

1. Scrubbing Optimization via Availability Prediction (SOAP) for Reconfigurable Space Computing Quinn Martin Alan George

2. SOAP • Background • FPGAs and Radiation in Space • Traditional Scrubbing Methods • SOAP Approach • Mission Parameters • Markov Models • Mission Case Studies • Results • Conclusions

3. FPGAs • Field-Programmable Gate Arrays (FPGAs) • Implement custom digital logic hardware with fabric of logic resources and interconnect • Lookup tables (LUTs) implement combinational logic • User flip flops (FFs) implement sequential logic • Switch and connection boxes route among resources • Many are reconfigurable • Allows update of routing and logic state • Partial reconfiguration can update partition of device • E.g., Virtex from Xilinx and Stratix from Altera

4. Reconfigurable FPGAs in Space • Advantages • Very high performance/power ratio • Reconfigurable (fully and partially) • Adaptable to changing environments and mission requirements • Can update design after launch • Disadvantages • Relatively difficult to design/test applications • Configuration memory vulnerable to radiation • Can change application processor architecture in unpredictable way • Must repair upsets via configuration scrubbing

5. Radiation Effects on FPGAs • Single-event Effects (SEE) • Single-event Latchup (SEL) – Causes current spike that may damage device • Single-event Upset (SEU) – Changes state of bit(s), e.g. from logic ‘0’ to ‘1’ • Can be single-bit upset (SBU) or multi-bit upset (MBU) • Single-event Functional Interrupt (SEFI) – Like SEU, but affecting critical device resource • Total Ionizing Dose • Degrades performance over time leading to eventual device failure

6. Xilinx V-5/V-6 Configuration • Programmed via SelectMAP interface • Runtime configuration interface • Also allows readback of existing configuration • 32 bits per configuration word • Parallel bus width of 8, 16, or 32 bits • Max clock frequency 100 MHz • Configuration memory arranged in frames • Minimum unit of access to config. memory • Virtex-5 – 41 words per frame • Virtex-6 – 81 words per frame

7. FPGA Scrubbing • FPGA Configuration Scrubbing • Quickly repairs SEUs before accumulation • Accumulation defeats redundancy strategies (e.g., TMR) • Fast repair can prevent SEUs from manifesting as errors • Can be decomposed into basic scrubbing techniques • Correction techniques repair upsets • Detection techniques discover and locate upsets

8. FPGA Scrubbing Techniques • Correction Techniques • Golden Copy – Repairs configuration based on know “golden” copy (e.g., in rad-hard PROM) • Frame ECC – Repairs based on per-frame error syndrome code stored on-chip • Detection Techniques • Frame ECC – Detects based on per-frame SECDED Hamming code • CRC-32– Detects using device-wide CRC-32

9. FPGA Scrubbing Strategies • Scrubbing Strategies • Any combination of detection and correction techniques with controller to implement algorithm • Blind Scrubbing – Golden copy correction only • Readback Scrubbing – Some detection technique used

10. FPGA Scrubbing Strategies

11. SOAP Approach • Scrubbing Optimization via Availability Prediction (SOAP) • Uses system availability as primary metric for scrubbing efficacy • Models scrubbing strategies as Markov diagrams • Vary free parameters to find optimal scrubbing system • Environmental parameters λ and α (orbits) • System parameters B and fCCLK (memory and pin constraints) • Scrubbing parameters μ and γ (device configuration capability)

12. SOAP Approach

13. Environmental Parameters • λ - SEU rates for devices in various orbits of interest • Calculated per-bit and per-device using CREME96 • α – Correction factors for single-bit and multi-bit upsets (SBU/MBU) • From beam tests on Virtex-5 devices

14. System Parameters • Factors chosen by the system designer based on available memories, power budget, etc. • Affect scrubbing detection and correction rates (see equations on next slide) • B – Configuration bus width in bits • fCCLK – Configuration clock speed in Hz

15. Scrubbing Parameters • μ – Repair rate for scrubbing technique (per second) • γ – Detection rate for scrubbing technique (per second)

16. Markov Algorithm Models • Blind • No detection • Built-in CRC-32 • Basic detection • Frame ECC with CRC-32 • CRC acts as “safety net” for upsets undetected by Frame ECC • Frame ECC with CRC-32 and Essential Bits (EB) • Only scrubs errors that may be critical

17. Blind Scrubbing

18. Readback CRC-32 Scrubbing

19. CRC-32 w/ Frame ECC Scrubbing

20. Case Study • Applies SOAP method to hypothetical systems with realistic parameters • Devices • Xilinx Virtex-5 • Xilinx Virtex-6 • Orbits • ISS low earth orbit (LEO) • Molniya highly elliptical orbit (HEO) • 8-bit SelectMAP bus at 33 MHz • Accounts for access speed of slow rad-hard PROM

21. Case Study • Two mission types • Non upset critical (non-UC) – System continues to run upon detection and correction of upset • Only count critical upsets as system “unavailable” • Upset critical (UC) – System requires reset upon detection of upset to ensure state integrity • Requires detection • All detected upsets render system unavailable for reset period • Will benefit from essential bits mask used in detection

22. Non-UC Results • Continuous blind scrubbing offers highest availability • CRC-32 offers similar availability with low implementation complexity • Frame ECC suffers because TBUs can be falsely corrected, resulting in further errors

23. UC Results

24. UC Results

25. Results • Frame ECC with CRC-32 and Essential Bits mask offers highest availability • Roughly one extra nine over other methods • Xilinx-provided soft-error mitigation (SEM) core implements similar strategy • Other strategies still competitive • Complex state machine or software and additional memory required for Frame ECC/EB • Model does not account for vulnerability associated with internal scrubbing

26. Conclusions • Predicts availability for various FPGA scrubbing strategies on real and hypothetical platforms • Uses analytical models rather than experimentation • Markov availability modeling with parametric approach • Allows optimization of scrubbing strategy during design phase • In case study, blind scrubbing best for non-UC and Frame ECC with EB mask best for UC