Date of Award
Doctor of Philosophy
Electrical and Computer Engineering
In modern processor architectures, caches are widely used to shorten the gap between the processor speed and memory access time. However, caches are time unpredictable, especially the shared L2 cache used by different cores on multicore processors. Thus, it can significantly increase the complexity of worst-case execution time (WCET) analysis, which is crucial for real-time systems. This dissertation designs several time-predictable scratchpad memory (SPM) based architectures for both VLIW (Very Long InstructionWord) based single-core and multicore processors. First, this dissertation proposes a time predictable two-level SPM based architecture for VLIW based single-core processors, and an ILP (Integer Linear Programming) based static memory objects allocation algorithm is extended to support the multi-level SPMs without harming the time predictability of SPMs. Second, several SPM based architectures for VLIW based multicore processors are designed. To support these architectures, the dynamic memory objects allocation based partition, the static memory objects allocation based partition and the static memory objects allocation based priority L2 SPM strategy are proposed, which retain the characteristic of time predictability. Also, both the WCET and worst-case energy consumption (WCEC) of our SPM based single-core and multicore architectures are completely evaluated in this dissertation. Last, to exploit the load/store latencies that are statically known in this architecture, we study a SPM-aware scheduling method to improve the performance. Our experimental results indicate the strengths and weaknesses of each proposed architecture and allocation method, which offers interesting memory design options to enable real-time computing. The strength of the two-level architecture is its superior performance compared to the one-level architecture, while the strength of the one-level architecture is its simple implementation. Also, the two-level architecture with separated L1 SPM for each core better fits for the data-intensive real-time applications, which not only retains good performance but also achieves a higher bandwidth by accessing both instruction and data SPM at the same time. Compared to the static based strategies, the dynamic allocation based partition L2 SPM strategy offers the better performance on each core because of the reuse of SPM space at the run-time, but has much higher complexity. In addition, the experimental results show that the timing and energy performance of our proposed SPM based architectures are superior to the similar cache based and hybrid architectures. Meanwhile, our architectures can ensure time predictability which is desirable for the real-time systems.
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