Error Resilient GPU Accelerated Image Processing for Space Applications

Significant advances in spaceborne imaging payloads have resulted in new big data problems in the Earth Observation (EO) field. These challenges are compounded onboard satellites due to a lack of equivalent advancement in onboard data processing and downlink technologies. We have previously proposed a new GPU accelerated onboard data processing architecture and developed parallelised image processing software to demonstrate the achievable data processing throughput and compression performance. However, the environmental characteristics are distinctly different to those on Earth, such as available power and the probability of adverse single event radiation effects. In this paper, we analyse new performance results for a low power embedded GPU platform, investigate the error resilience of our GPU image processing application and offer two new error resilient versions of the application. We utilise software based error injection testing to evaluate data corruption and functional interrupts. These results inform the new error resilient methods that also leverages GPU characteristics to minimise time and memory overheads. The key results show that our targeted redundancy techniques reduce the data corruption from a probability of up to 46 percent to now less than 2 percent for all test cases, with a typical execution time overhead of 130 percent.