Achieving Dependability of AI Execution with Radiation Hardened Processors
Fuente:
arXiv
Enregistré dans:
| Auteurs principaux: | , , , |
|---|---|
| Format: | Preprint |
| Publié: |
2025
|
| Sujets: | |
| Accès en ligne: | |
| Tags: |
Ajouter un tag
Pas de tags, Soyez le premier à ajouter un tag!
|
| _version_ | 1866910904093769728 |
|---|---|
| author | Taquichiri, Carlos Rafael Tordoya Doran, Hans Dermot Ghiglino, Pablo Harshe, Mandar |
| author_facet | Taquichiri, Carlos Rafael Tordoya Doran, Hans Dermot Ghiglino, Pablo Harshe, Mandar |
| contents | The reliance on radiation-hardened hardware, essential for domains requiring high-dependability such as space, nuclear energy and medical applications, severely restricts the choice of components available for modern AI-intensive tasks, particularly for real-time AI-based classifications. To address this challenge, we propose leveraging the High Performance Data Processor (HPDP) as a radiation-hardened and low-power co-processor in conjunction with an optimized AI framework for efficient data processing. The HPDP's dynamic reconfiguration capabilities and dataflow-oriented architecture provide an ideal platform for executing AI-driven applications that demand low-latency, high-throughput streaming data processing. To fully utilize the co-processor's capabilities, we utilized Klepsydra's AI-runtime inference framework, which, due to its lock-free execution and efficient resource management, significantly enhances data processing throughput without increasing power consumption. Our approach entails programming the HPDP as a dedicated mathematical backend, enabling the AI framework to execute workloads directly on this co-processor without requiring additional hardware-specific coding. This paper presents the preliminary results of our implementation, describing the application domain, AI pipeline, key features of the HPDP architecture, and performance evaluation. Our solution demonstrates a significant advancement in deploying AI on radiation-hardened platforms by using the HPDP as a dependable, efficient, and reprogrammable co-processor, making it highly suitable for any application requiring dependable execution in any environment. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2504_03680 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Achieving Dependability of AI Execution with Radiation Hardened Processors Taquichiri, Carlos Rafael Tordoya Doran, Hans Dermot Ghiglino, Pablo Harshe, Mandar Distributed, Parallel, and Cluster Computing Hardware Architecture The reliance on radiation-hardened hardware, essential for domains requiring high-dependability such as space, nuclear energy and medical applications, severely restricts the choice of components available for modern AI-intensive tasks, particularly for real-time AI-based classifications. To address this challenge, we propose leveraging the High Performance Data Processor (HPDP) as a radiation-hardened and low-power co-processor in conjunction with an optimized AI framework for efficient data processing. The HPDP's dynamic reconfiguration capabilities and dataflow-oriented architecture provide an ideal platform for executing AI-driven applications that demand low-latency, high-throughput streaming data processing. To fully utilize the co-processor's capabilities, we utilized Klepsydra's AI-runtime inference framework, which, due to its lock-free execution and efficient resource management, significantly enhances data processing throughput without increasing power consumption. Our approach entails programming the HPDP as a dedicated mathematical backend, enabling the AI framework to execute workloads directly on this co-processor without requiring additional hardware-specific coding. This paper presents the preliminary results of our implementation, describing the application domain, AI pipeline, key features of the HPDP architecture, and performance evaluation. Our solution demonstrates a significant advancement in deploying AI on radiation-hardened platforms by using the HPDP as a dependable, efficient, and reprogrammable co-processor, making it highly suitable for any application requiring dependable execution in any environment. |
| title | Achieving Dependability of AI Execution with Radiation Hardened Processors |
| topic | Distributed, Parallel, and Cluster Computing Hardware Architecture |
| url | https://arxiv.org/abs/2504.03680 |