# Performance Evaluation Method for Intelligent Computing Components for Space Applications
## 2 Related Work  
Studies in references [11; 12] evaluate the performance of COTS devices such as CPU, DSP, GPU, and FPGA based on data provided by suppliers. Using a set of established device metrics, different processor architectures are quantitatively analyzed and comparedin terms of performance, power, efficiency, memory bandwidth, input-output bandwidth, and other aspects. However, vendor-supplied data and established metrics are often limited and lack consideration of deeper application directions such as space autonomous computing tasks. The Institute of Computing Technology of the Chinese Academy of Sciences, led by [PERSON], has developed the mobile embedded device benchmark suite A IoT Bench [13], which aims to evaluate the artificial intelligence capabilities of mobile and embedded devices in image classification, speech recognition, converter translation, and micro-processing loads. The Embedded Microprocessor Benchmark Consortium (EEMBC) provides many benchmarks, such as ADAS Mark and ML Mark. The ADAS Mark benchmark is a performance measurement and optimization tool for building next-generation advanced driver assistance systems (ADAS) [14]. The image processing in this benchmark is close to what is carried out on spacecrafts, but it does not fully map to specific image processing tasks used in space applications. ML Mark is a benchmark for edge machine learning (ML) tasks [15]. ML workloads are trained based on space-independent ILSVRC2012 and COCO2014 datasets, and they do not consider typical sensor sizes used in space missions.  
In the embedded CPU industry, the following three performance testing standards are widely recognized: Coremark, Dhrystone, and Coremark-Pro. Currently, Coremark-Pro has been used in the space domain, and examples of single-core and multi-core LEON processor (maintained by Gaisler Research) Coremark-Pro results are provided in [16; 17]. Spacebench [18] encompasses a range of typical computations involving integers and floating-point numbers that are commonly employed in the realm of space computing. The open-source project GPU4S (GPU for Space), supported by the European Space Agency, provides a benchmark test that covers representative space algorithms in different space domains, used to evaluate GPU programming models for space payload processing [19]. OBPMARKs (On-Board Processing Benchmarks) was developed based on GPU4S and defines a set of benchmark testing methods covering common typical applications in space missions. Performance evaluations are carried out for high-performance processors such as ARM Mali G-72 (Cambridge, UK), NVIDIA Xavier NX (Santa Clara, CA, USA), and NVIDIA TX2 [20].  
The computational performance requirements for space missions are constantly increasing, so a reasonable method for effectively characterizing the performance of new-generation COTS processors is essential. Based on the aforementioned investigation and analysis, the objective of this paper is to construct benchmarks for space applications and perform performance evaluations on currently available high-performance processors. The structure of the remaining part of this paper is as follows: Section 3 introduces the basic principles of performance evaluation, and determines benchmark testing methods and performance metrics; Section 4 presents the evaluation targets and experimental results of performance evaluation; finally, Section 5 provides a summary of the entire paper.