Using Scratchpad Memory for Stack Data in Hard Real-Time Embedded Systems

نویسنده

  • Sungjun Kim
چکیده

This paper proposes a scratchpad memory allocation technique for stack data to achieve predictability for hard real-time embedded systems. First, we suggest a profiling-based source code modification technique to make the whole stack area fit in the given scratchpad memory size. Furthermore, even though the whole stack area cannot fit in the scratchpad memory size, we propose another scratchpad memory allocation to achieve predictability when an end-toend measurement-based WCET analysis is used. Lastly, we compared performance of our design with data caches. Keywords-hard real-time systems; embedded systems; scratchpad memory; predictability;

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تاریخ انتشار 2011