Due to the trends of centralizing the E/E architecture and new computing-intensive applications, high-performance hardware platforms are currently finding their way into automotive systems. However, the SoCs currently available on the market have significant weaknesses when it comes to providing predictable performance for time-critical applications. The main reason for this is that these platforms are optimized for averagecase performance. This shortcoming represents one major risk in the development of current and future automotive systems. In this paper we describe how high-performance and predictability could (and should) be reconciled in future HW/SW platforms. We believe that this goal can only be reached in a close collaboration between system suppliers, IP providers, semiconductor companies, and OS/hypervisor vendors. Furthermore, academic input will be needed to solve remaining challenges and to further improve initial solutions.
The Road towards Predictable Automotive High-Performance Platforms
Giovanni Stea;
2021-01-01
Abstract
Due to the trends of centralizing the E/E architecture and new computing-intensive applications, high-performance hardware platforms are currently finding their way into automotive systems. However, the SoCs currently available on the market have significant weaknesses when it comes to providing predictable performance for time-critical applications. The main reason for this is that these platforms are optimized for averagecase performance. This shortcoming represents one major risk in the development of current and future automotive systems. In this paper we describe how high-performance and predictability could (and should) be reconciled in future HW/SW platforms. We believe that this goal can only be reached in a close collaboration between system suppliers, IP providers, semiconductor companies, and OS/hypervisor vendors. Furthermore, academic input will be needed to solve remaining challenges and to further improve initial solutions.File | Dimensione | Formato | |
---|---|---|---|
DATE2021_special_session_Paper.pdf
accesso aperto
Descrizione: postprint
Tipologia:
Documento in Post-print
Licenza:
Tutti i diritti riservati (All rights reserved)
Dimensione
353.57 kB
Formato
Adobe PDF
|
353.57 kB | Adobe PDF | Visualizza/Apri |
2021 DATE_special_session_paper_2022_final.pdf
solo utenti autorizzati
Descrizione: versione IEEE Xplore
Tipologia:
Versione finale editoriale
Licenza:
NON PUBBLICO - Accesso privato/ristretto
Dimensione
477.64 kB
Formato
Adobe PDF
|
477.64 kB | Adobe PDF | Visualizza/Apri Richiedi una copia |
I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.