Please use this identifier to cite or link to this item: https://hdl.handle.net/10356/155574
Title: MARCO : a high-performance task mapping and routing co-optimization framework for point-to-point NoC-based heterogeneous computing systems
Authors: Chen, Hui
Zhang, Zihao
Chen, Peng
Luo, Xiangzhong
Li, Shiqing
Liu, Weichen
Keywords: Engineering::Computer science and engineering
Issue Date: 2021
Source: Chen, H., Zhang, Z., Chen, P., Luo, X., Li, S. & Liu, W. (2021). MARCO : a high-performance task mapping and routing co-optimization framework for point-to-point NoC-based heterogeneous computing systems. ACM Transactions On Embedded Computing Systems, 20(5s), 54-. https://dx.doi.org/10.1145/3476985
Project: MoE2019-T2-1-071
MoE2019-T1-1-072
M4082282
M4082087
Journal: ACM Transactions on Embedded Computing Systems
Abstract: Heterogeneous computing systems (HCSs), which consist of various processing elements (PEs) that vary in their processing ability, are usually facilitated by the network-on-chip (NoC) to interconnect its components. The emerging point-to-point NoCs which support single-cycle-multi-hop transmission, reduce or eliminate the latency dependence on distance, addressing the scalability concern raised by high latency for long-distance transmission and enlarging the design space of the routing algorithm to search the non-shortest paths. For such point-to-point NoC-based HCSs, resource management strategies which are managed by compilers, scheduler, or controllers, e.g., mapping and routing, are complicated for the following reasons: (i) Due to the heterogeneity, mapping and routing need to optimize computation and communication concurrently (for homogeneous computing systems, only communication). (ii) Conducting mapping and routing consecutively cannot minimize the schedule length in most cases since the PEs with high processing ability may locate in the crowded area and suffer from high resource contention overhead. (iii) Since changing the mapping selection of one task will reconstruct the whole routing design space, the exploration of mapping and routing design space is challenging. Therefore, in this work, we propose MARCO, the mapping and routing co-optimization framework, to decrease the schedule length of applications on point-to-point NoC-based HCSs. Specifically, we revise the tabu search to explore the design space and evaluate the quality of mapping and routing. The advanced reinforcement learning (RL)algorithm, i.e., advantage actor-critic, is adopted to efficiently compute paths. We perform extensive experiments on various real applications, which demonstrates that the MARCO achieves a remarkable performance improvement in terms of schedule length (+44.94% ∼+50.18%) when compared with the state-of-the-art mapping and routing co-optimization algorithm for homogeneous computing systems. We also compare MARCO with different combinations of state-of-the-art mapping and routing approaches.
URI: https://hdl.handle.net/10356/155574
ISSN: 1539-9087
DOI: 10.1145/3476985
DOI (Related Dataset): 10.21979/N9/TGQNYS
Rights: © 2021 Association for Computing Machinery. All rights reserved. This paper was published in ACM Transactions on Embedded Computing Systems and is made available with permission of Association for Computing Machinery.
Fulltext Permission: open
Fulltext Availability: With Fulltext
Appears in Collections:SCSE Journal Articles

Page view(s)

80
Updated on Aug 7, 2022

Download(s)

14
Updated on Aug 7, 2022

Google ScholarTM

Check

Altmetric


Plumx

Items in DR-NTU are protected by copyright, with all rights reserved, unless otherwise indicated.