In modern heterogeneous MPSoCs, the management of shared memory resources is crucial in delivering end-to-end QoS. Previous frameworks have either focused on singular QoS targets or the allocation of partitionable resources among CPU applications at relatively slow timescales. However, heterogeneous MPSoCs typically require instant response from the memory system where most resources cannot be partitioned. Moreover, the health of different cores in a heterogeneous MPSoC is often measured by diverse performance objectives. In this work, we propose the Self-Aware Resource Allocation framework for heterogeneous MPSoCs. Priority-based adaptation allows cores to use different target performance and self-monitor their own intrinsic health. In response, the system allocates non-partitionable resources based on priorities. The proposed framework meets a diverse range of QoS demands from heterogeneous cores. Moreover, we present a runtime scheme to configure priority-based adaptation so that distinct sensitivities of heterogeneous QoS targets with respect to memory allocation can be accommodated. In addition, the priority of best-effort cores can also be regulated.

In heterogeneous multicore systems, the memory subsystem, including the last-level cache and DRAM, is widely shared among the CPU, the GPU, and the real-time cores. Due to their distinct memory traffic patterns, heterogeneous cores result in more frequent cache misses at the last-level cache. As cache misses travel through the memory subsystem, two schedulers are involved for the last-level cache and DRAM, respectively. Prior studies treated the scheduling of the last-level cache and DRAM as independent stages. However, with no orchestration and limited visibility of memory traffic, neither scheduling stage is able to ensure optimal scheduling decisions for memory efficiency. Unnecessary precharges and row activations happen in DRAM when the memory scheduler is ignorant of incoming cache misses, and DRAM row-buffer states are invisible to the last-level cache. In this article, we propose a unified memory controller for the the last-level cache and DRAM with orchestrated schedulers. The memory scheduler harvests row-buffer hit opportunities in cache request buffers during spare time without inducing significant implementation cost. We further introduce a dynamic orchestrated scheduling policy to improve memory efficiency while achieving target CPU IPC. Extensive evaluations show that the proposed controller improves the total memory bandwidth of DRAM by 16.8% on average and saves DRAM energy by up to 29.7% while achieving comparable CPU IPCs. With the dynamic scheduling policy, the unified controller achieves the same IPC as the conventional design and increases DRAM bandwidth by 9.2%. In addition, we explore the potential of the proposed memory controller to attain improvements on both memory bandwidth and CPU IPC.

In modern heterogeneous MPSoCs, the management of shared memory resources is crucial in delivering end-to-end QoS. Previous frameworks have either focused on singular QoS targets or the allocation of partitionable resources among CPU applications at relatively slow timescales. However, heterogeneous MPSoCs typically require instant response from the memory system where most resources cannot be partitioned. Moreover, the health of different cores in a heterogeneous MPSoC is often measured by diverse performance objectives. In this work, we propose the Self-Aware Resource Allocation (SARA) framework for heterogeneous MPSoCs. Priority-based adaptation allows cores to use different target performance and self-monitor their own intrinsic health. In response, the system allocates non-partitionable resources based on priorities. The proposed framework meets a diverse range of QoS demands from heterogeneous cores.

In heterogeneous multicore systems, the memory subsystem, including the last-level cache and DRAM, is widely shared among the CPU, the GPU, and the real-time cores. Due to their distinct memory traffic patterns, heterogeneous cores result in more frequent cache misses at the last-level cache. As cache misses travel through the memory subsystem, two schedulers are involved for the last-level cache and DRAM respectively. Prior studies treated the scheduling of the last-level cache and DRAM as independent stages. However, with no orchestration and limited visibility of memory traffic, neither scheduling stage is able to ensure optimal scheduling decisions for memory efficiency. Unnecessary precharges and row activations happen in DRAM when the memory scheduler is ignorant of incoming cache misses and DRAM row-buffer states are invisible to the last-level cache. In this paper, we propose a unified memory controller for the the last-level cache and DRAM with orchestrated schedulers. The memory scheduler harvests row-buffer hit opportunities in cache request buffers during spare time without inducing significant implementation cost. Extensive evaluations show that the proposed controller improves the total memory bandwidth of DRAM by 16.8% on average and saves DRAM energy by up to 29.7% while achieving comparable CPU IPC. In addition, we explore the impact of last-level cache bypassing techniques on the proposed memory controller.