Full metadata record
| DC Field | Value | Language |
|---|---|---|
| dc.contributor.author | Tordoya Taquichiri, Carlos Rafael | - |
| dc.contributor.author | Ganz, David | - |
| dc.date.accessioned | 2024-06-07T14:34:55Z | - |
| dc.date.available | 2024-06-07T14:34:55Z | - |
| dc.date.issued | 2024-05-28 | - |
| dc.identifier.uri | https://digitalcollection.zhaw.ch/handle/11475/30807 | - |
| dc.description.abstract | In challenging domains requiring high-dependability, such as space and some medical applications, the reliance on radiation-hardened components, such as processors and memory, restricts the choice of hardware for implementing modern and computationally intensive algorithms, particularly AI-based classifications with real-time constraints. To address this limitation we propose the adaptation of a well-understood AI-runtime inference framework and Streaming Distribution Optimiser (SDO), both from Klepsydra, for execution on the High Performance Data Processor (HPDP), a radiation-hardened co-processor known for its low power consumption, flexibility, and run-time re-programmability. The AI-pipeline has demonstrated a significant increase in data processing rates (up to 10x) and a 50% reduction in energy consumption on standard computing hardware by utilizing lock-free execution. The SDO, utilizes Genetic Algorithms (GA) to optimize the distribution of computing resources for executing AI models on the target processor, prioritizing factors such as latency, power consumption, and/or data rate. Our proposed solution entails a port of the AI-pipeline and SDO for the HPDP which eliminates the need for HPDP-specific coding and ensure compatibility with major AI frameworks. We present the results of our implementation, outlining the application domain, the optimized AI pipeline, and the key features of the HPDP processor. The structured partitioning of the AI pipeline across various processors and functional units is described, along with performance measurements and conclusions. Our proposed solution simplifies the deployment of AI models on radiation-hardened processing platforms making it an attractive option for European Space Agency (ESA) future missions with high availability requirements, such as landers and deep space robotics. This hardware/software stack represents a fully European solution, enhancing Europe's capacity to leverage AI in space applications. | de_CH |
| dc.language.iso | en | de_CH |
| dc.rights | Licence according to publishing contract | de_CH |
| dc.subject | Artificial Intelligence (AI) | de_CH |
| dc.subject | Space | de_CH |
| dc.subject | Acceleration | de_CH |
| dc.subject | High Performance Data Processor (HPDP) | de_CH |
| dc.subject | Performance | de_CH |
| dc.subject | Radiation | de_CH |
| dc.subject | XPP | de_CH |
| dc.subject | Processor | de_CH |
| dc.subject.ddc | 006: Spezielle Computerverfahren | de_CH |
| dc.title | AI accelleration for space applications | de_CH |
| dc.type | Konferenz: Sonstiges | de_CH |
| dcterms.type | Text | de_CH |
| zhaw.departement | School of Engineering | de_CH |
| zhaw.organisationalunit | Institute of Embedded Systems (InES) | de_CH |
| zhaw.conference.details | Embedded Computing Conference (ECC), Winterthur, Switzerland, 28 May 2024 | de_CH |
| zhaw.funding.eu | No | de_CH |
| zhaw.originated.zhaw | Yes | de_CH |
| zhaw.publication.status | publishedVersion | de_CH |
| zhaw.publication.review | Peer review (Abstract) | de_CH |
| zhaw.author.additional | No | de_CH |
| zhaw.display.portrait | Yes | de_CH |
| Appears in collections: | Publikationen School of Engineering | |
Files in This Item:
There are no files associated with this item.
Show simple item record
Tordoya Taquichiri, C. R., & Ganz, D. (2024, May 28). AI accelleration for space applications. Embedded Computing Conference (ECC), Winterthur, Switzerland, 28 May 2024.
Tordoya Taquichiri, C.R. and Ganz, D. (2024) ‘AI accelleration for space applications’, in Embedded Computing Conference (ECC), Winterthur, Switzerland, 28 May 2024.
C. R. Tordoya Taquichiri and D. Ganz, “AI accelleration for space applications,” in Embedded Computing Conference (ECC), Winterthur, Switzerland, 28 May 2024, May 2024.
TORDOYA TAQUICHIRI, Carlos Rafael und David GANZ, 2024. AI accelleration for space applications. In: Embedded Computing Conference (ECC), Winterthur, Switzerland, 28 May 2024. Conference presentation. 28 Mai 2024
Tordoya Taquichiri, Carlos Rafael, and David Ganz. 2024. “AI Accelleration for Space Applications.” Conference presentation. In Embedded Computing Conference (ECC), Winterthur, Switzerland, 28 May 2024.
Tordoya Taquichiri, Carlos Rafael, and David Ganz. “AI Accelleration for Space Applications.” Embedded Computing Conference (ECC), Winterthur, Switzerland, 28 May 2024, 2024.
Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.