SARA: Self-Aware Resource Allocation for Heterogeneous MPSoCs
Yang Song, Olivier Alavoine, Bill Lin

TL;DR
SARA is a self-aware framework for resource allocation in heterogeneous MPSoCs that dynamically manages shared memory to meet diverse QoS and health objectives.
Contribution
It introduces a priority-based, self-monitoring resource allocation framework tailored for heterogeneous MPSoCs with non-partitionable resources.
Findings
Successfully meets diverse QoS demands
Adapts to core health and performance objectives
Improves resource utilization efficiency
Abstract
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 a 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…
Peer Reviews
No public reviews on file for this paper yet. If you reviewed it on a platform where reviews are public (OpenReview, ICLR, NeurIPS, ICML), you can paste yours below so the community can read it here.
Videos
No videos yet. Explain this paper in a talk, walkthrough, or lecture? Add one.
