The main objectives of the activity cover the following aspects:
Generation of a reusable and open reference dataset:
For this activity several different use-cases have been proposed to be selected for the benchmarking and shall be made publicly available during the project: 1) Pose-estimation, 2) Anomaly-detection and 3) Image classification.
Evaluation and selection of suitable target platforms:
Huge efforts are made to build radiation tolerant and/or hard processor units as well as to evaluate, alter and qualify specific COTS processors. For the activity three different processor units are proposed, each covering a different performance area: low-performance, mid-performance and high-performance. All pre-selected processor units have a direct relation to be potentially applicable in space missions.
Development of ML algorithms representative for tasks required for future space missions:
It is understood that the benchmarking shall contain representative applications which are typical for future space-applications of AI techniques. Therefore, it is planned to start with a detailed analysis of the requirements from a various set of space-applications which cover high-performant and mission-critical on-board applications to low-performance and less mission-critical.
Development of an efficient and reliable method of ML task management on the target platform:
It is planned to make developed inference models interchangeable on the target platforms. Furthermore, it is proposed to deploy developed models for future use cases on the target platforms. This deployment will be controlled via the EGSE GUI in to ensure ease of use and reliable operation of the entire task management and configuration process.
Development and execution of a benchmarking solution for HW suitable target applications:
It is foreseen to specify a maximum workload for each benchmark on all selected AI accelerator H/W devices. The use-cases will be defined that different amounts of operations have to be performed. There are two type of metrics to be considered. The metric from computational point of view and the performance metrics such as accuracy and precision For each benchmark, at least one metric will be specified Those post-processing functions will be part of the benchmark metric definition.
Preparation of a ML accelerator demonstrator:
To perform the benchmark, a ML accelerator demonstrator will be deployed. It comprises all preselected hardware platforms as well as a central EGSE workstation. The demonstrator will be designed in a generic manner, allowing to add further hardware platforms and/or use-cases for the benchmark in the future.
Evaluation of results and provision of standard inference workflow:
The ML accelerator demonstrator serves as a first initialization and demonstration of a hardware and ML benchmark for space applications. In order to add further use-cases and hardware platforms in the future, a standardized inference workflow will be evaluated and described as a result of the activity.
Airbus Defence and Space