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REVIEW 2 major objections 6 minor 1 cited by

A Survey of Real-time Scheduling on Accelerator-based Heterogeneous Architecture for Time Critical Applications

T0 review · 2 major / 6 minor · reviewed 2026-08-15 · deepseek-v4-flash

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Pith's one-line read This survey maps ten years of real-time scheduling on accelerator-based heterogeneous platforms, organizing the field by soft versus hard deadlines, task execution models, and application domains.

desk verdict A genuinely useful, well-organized survey that makes a few internal-consistency mistakes in its stated scope; worth refereeing, but the authors should fix the latency/QoS inclusion contradiction before publication. read the letter →

arxiv 2505.11970 v1 pith:B3GRN3KW submitted 2025-05-17 cs.DC cs.AR

An Zou , Yuankai Xu , Yinchen Ni , Jintao Chen , Yehan Ma , Jing Li , Christopher Gill , Xuan Zhang
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Yier Jin
This is my paper · ORCID
classification cs.DCcs.AR
keywords real-timeschedulingheterogeneouscomputingGPUFPGATPUself-suspendingtasksDAGtaskchain
verification ladder T0 review T1 audit T2 compute T3 formal

The pith

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

The reading

The paper tries to establish that real-time scheduling on accelerator-based heterogeneous systems--chips that combine CPU cores with GPUs, TPUs, or FPGAs--has matured into a coherent research area that can be mapped by deadline type, task model, and application. It argues that the last decade divides naturally into soft-real-time work, which improves schedulability without strict response-time analysis, and hard-real-time work, which carries formal worst-case response-time guarantees, with a third stream driven by applications such as autonomous driving, perception, language models, satellites, and extended reality. A sympathetic reader would care because the survey supplies the categories and reference points needed to locate where a new scheduling problem fits and which existing techniques are candidates to extend.

What carries the argument

The organizing machinery is a two-axis taxonomy: deadline class (soft versus hard) crossed with accelerator count (single versus multiple), task execution model, and objective (timing only versus energy or thermal). The load-bearing task models are the self-suspending segmented model $\tau_i = (C^1_i, A^1_i, C^2_i, \ldots, A^{M_i-1}_i, C^{M_i}_i), D_i, T_i$, the directed acyclic graph (DAG) model, and the task-chain model, each mapped onto a real workload such as CNN inference, transformer attention, or ROS pipelines. The taxonomy does the work: it converts a list of papers into a decision tree that a researcher can enter with their deadline type, task shape, and accelerator count.

What would settle it

Run a systematic literature search over 2015-2025 for deadline-constrained scheduling on GPU, FPGA, TPU, or NPU platforms; if the majority of retrieved works are latency-, QoS-, or virtualization-driven, the survey's scope decision would place the field's center of gravity outside the map it claims to draw.

Watch

Extended reading notes

Core claim

On the paper's own terms, the discovery is organizational: the seemingly scattered literature on scheduling real-time tasks on CPU-plus-accelerator platforms can be read as a progression from architectural features through task models to scheduling objectives. It shows that three task models--the segmented self-suspension model, the directed acyclic graph (DAG) model, and the task-chain model--capture most existing analyses, and that soft-real-time designs (vendor schedulers, priority queues, spatial partitioning, energy and thermal heuristics) and hard-real-time designs (fixed-priority and EDF analyses, response-time bounds, multi-objective thermal and energy control) form distinct lineages. It also claims that application-driven work is where the field is currently expanding, and it closes by identifying open problems: platforms with more than two processor types, memory-bus copy overhead, co-designed CPU-plus-accelerator metrics, and standardized evaluation.

Load-bearing premise

The survey's claim to be comprehensive depends on its explicit decision to exclude latency- and QoS-driven scheduling and cloud-based virtualized accelerator servers, so if a large share of time-critical accelerator scheduling research falls in those excluded areas, the map is incomplete.

Editorial extensions

If this is right

  • A researcher with a new hard-deadline single-accelerator problem can locate the relevant analysis lineage, such as fixed-priority or EDF-like response-time bounds over self-suspending segments, without re-deriving the field.
  • Multi-accelerator systems shift the focus from response-time analysis to task-to-accelerator allocation, so new work there should expect to compare against allocation and load-balancing approaches rather than purely analytical schedulability tests.
  • Soft-real-time designs that tolerate occasional deadline misses can use vendor-provided mechanisms as baselines before adopting heuristic frameworks that add preemption, spatial partitioning, or energy and thermal control.
  • Application-driven scheduling, covering autonomous systems, perception, language models, satellites, and extended reality, is presented as the current frontier where deadline type, task model, and platform constraints are chosen together.
  • The survey's open problems imply that the next advances are likely to come from platforms with more than two processor types, explicit memory-copy modeling in schedules, and standardized evaluation metrics that allow fair comparison.

Reading between the lines

Editorial extensions of the paper, not claims the author makes directly.

  • Editorial extension: the survey's observation that two different utilization definitions coexist, one CPU-dominant and one treating CPU and accelerator equally, suggests the field lacks a shared unit of load across processor types; a testable next step is a benchmark suite that reports both definitions and shows when scheduling conclusions flip.
  • Editorial extension: the task-chain model is likely to grow in importance as ROS 2 becomes the standard integration layer for robots using accelerators, so scheduling work that ignores chain-level data dependencies may increasingly miss the true end-to-end latency.
  • Editorial extension: because memory copies are modeled as non-preemptive stages, unified-memory platforms where CPU and accelerator share addresses may eventually blur the CPU-segment versus accelerator-segment distinction, which would strain the segmented model that much of the hard-real-time analysis relies on.
  • Editorial extension: a reader could test the taxonomy's completeness by applying it to papers outside the survey's scope, such as latency- or QoS-driven accelerator systems; if those papers resist the soft-versus-hard deadline distinction, the taxonomy would need a third axis.
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Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, and a circularity audit.

Referee Report

2 major / 6 minor

Summary. The paper surveys real-time scheduling techniques for accelerator-based heterogeneous architectures, covering CPU-GPU, CPU-TPU, and CPU-FPGA systems. It organizes the material around task execution models (accelerator-only, self-suspending segmented model, DAG, and task chain), soft versus hard deadline classes, vendor and research-designed schedulers, multi-objective approaches (energy and thermal), and application-driven scheduling for autonomous systems, perception, language models, satellites, and extended reality. The survey claims to be comprehensive for the past ten years and concludes with open challenges such as multi-core heterogeneity, memory-copy overheads, response-time analysis, and standardized evaluation.

Significance. If the scope were applied consistently, this would be a valuable reference map for researchers entering real-time scheduling on accelerator-based heterogeneous platforms. The paper's strengths are its broad collection of recent works, the explicit task-model taxonomy, the structured tables summarizing soft/hard real-time and application-driven schedulers, and the honest enumeration of open problems. It does not make new formal claims, so correctness hinges on accurate representation and consistent selection criteria; the tables and descriptions are generally faithful to the cited works.

major comments (2)
  1. [II.B.2 vs IV.B.2, VI.C, VI.E, Table IV] The stated inclusion criterion is inconsistent with several included works. Section II.B.2 says a task qualifies as real-time only if it has hard or soft deadlines, and that latency- or QoS-driven scheduling approaches are not covered. However, Pegasus (Section IV.B.2, Table II) is included with a policy named SLAF described as 'feedback-driven adjustments for QoS'; Llumnix (Section VI.C, Table IV) is included for 'differentiated Service Level Objectives (SLOs)' without any deadline model; and BOXR (Section VI.E, Table IV) is included on the basis of M2D/C2D latency budgets. These works are latency/QoS-driven by the survey's own definition, so the 'comprehensive survey' claim is underspecified: either the exclusion should be enforced or the scope should be broadened and explicitly justified.
  2. [II.B.2 vs IV.B.2 and VI.C] The same scope paragraph excludes cloud-based virtualized accelerator servers, yet Pegasus is a hypervisor-level scheduling system for virtualized GPU access and Llumnix is a datacenter-scale LLM serving system. This contradicts the stated emphasis on single-machine embedded and mobile real-time platforms. The authors should either remove these entries or revise the scope statement to acknowledge that some cloud/virtualized or latency-driven works are included because of their deadline-related contributions.
minor comments (6)
  1. [IV.B.1] The name 'Elloit' should be 'Elliott'; the same misspelling appears in Table II as 'Elliott [76] et al.'.
  2. [V.C, PredJoule paragraph] The phrase 'zhDNN stages' appears to be a typo and should be corrected to 'DNN stages' or the intended technical term.
  3. [III.B.3] The word 'espeacially' in the DAG model description should be 'especially'.
  4. [Table I] The entry 'Kernal' under the FRED row should be 'Kernel'.
  5. [Table IV] The scheduling algorithm label 'N P F Pf lex' has a formatting artifact and should read 'NPFPflex' consistently with the text in Section VI.B.
  6. [V.C] The phrase 'V oltage and Frequency Scaling' contains an unintended space and should be 'Voltage and Frequency Scaling'.

Circularity Check

0 steps flagged · score 0.0 of 10

No circularity: the survey makes no predictive or derivational claims; its inclusion criteria are internally inconsistent for a few QoS/latency-oriented works, but that is a coverage issue, not circular reasoning.

full rationale

This paper is a survey, not a derivation. It does not fit parameters to data, derive predictions from assumptions, or invoke a self-citation chain to force a conclusion. Its central claim, that it presents a comprehensive survey of real-time scheduling techniques for accelerator-based heterogeneous platforms, is supported by the organization of the reviewed literature rather than by any formal derivation. The scope statement in Section II.B.2 explicitly excludes latency- and QoS-driven scheduling approaches, yet later sections describe works such as Pegasus [79], Llumnix [112], and BOXR [114] using QoS, SLO, and latency language. That is an internal inconsistency in the survey's inclusion boundary and could weaken the comprehensiveness claim, but it is not circularity: the included works are external publications, and the survey does not define its object of study in terms of its own conclusions. Self-citations, such as references to RTGPU [58], SHAPE [59], and SCENIC [99], are used to describe prior work by the authors and to motivate open problems; none of these citations is load-bearing in the sense of replacing an argument with an unverified assertion by the same authors. No prediction, theorem, schedule, or schedulability result is produced in the paper, so there is no fitted input renamed as a prediction and no equation that reduces to its own input by construction. The honest finding is therefore no significant circularity.

Assumptions & free parameters 0 free parameters · 0 assumptions · 0 invented entities

This is a survey; it introduces no free parameters, derives no new equations, and posits no new entities. The task-model categories are the authors' organizational scheme, described from prior literature.

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Cite this review

Pith. "Pith review of A Survey of Real-time Scheduling on Accelerator-based Heterogeneous Architecture for Time Critical Applications." pith.science (2026). https://pith.science/paper/B3GRN3KW

@misc{pith2026250511970,
  author       = {Pith},
  title        = {Pith review of: A Survey of Real-time Scheduling on Accelerator-based Heterogeneous Architecture for Time Critical Applications},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/B3GRN3KW}},
  note         = {Machine review of arXiv:2505.11970}
}
read the original abstract

Accelerator-based heterogeneous architectures, such as CPU-GPU, CPU-TPU, and CPU-FPGA systems, are widely adopted to support the popular artificial intelligence (AI) algorithms that demand intensive computation. When deployed in real-time applications, such as robotics and autonomous vehicles, these architectures must meet stringent timing constraints. To summarize these achievements, this article presents a comprehensive survey of real-time scheduling techniques for accelerator-based heterogeneous platforms. It highlights key advancements from the past ten years, showcasing how proposed solutions have evolved to address the distinct challenges and requirements of these systems. This survey begins with an overview of the hardware characteristics and common task execution models used in accelerator-based heterogeneous systems. It then categorizes the reviewed works based on soft and hard deadline constraints. For soft real-time approaches, we cover real-time scheduling methods supported by hardware vendors and strategies focusing on timing-critical scheduling, energy efficiency, and thermal-aware scheduling. For hard real-time approaches, we first examine support from processor vendors. We then discuss scheduling techniques that guarantee hard deadlines (with strict response time analysis). After reviewing general soft and hard real-time scheduling methods, we explore application- or scenario-driven real-time scheduling techniques for accelerator-enabled heterogeneous computing platforms. Finally, the article concludes with a discussion of open issues and challenges within this research area.

Figures

Figures reproduced from arXiv: 2505.11970 by the authors.

Figure 1
Figure 1. Real-time scheduling of parallel tasks on the heterogeneous architecture. [PITH_FULL_IMAGE:figures/full_fig_p002_1.png] view at source ↗
Figure 2
Figure 2. Overview of this survey. general-purpose homogeneous processors, surveys have also addressed real-time scheduling on limited preemptive proces￾sors [20] and implementations of real-time scheduling in the Linux kernel [21]. (3) For artificial intelligence applications, earlier works have summarized scheduling and load-balancing strategies [22], [23], as well as neural network training tech￾niques [24], [25], primaril… view at source ↗
Figure 3
Figure 3. Different task modeling approaches. GPU research [52], [53]. In addition, researchers have pro￾posed architectural support for spatial partitioning in systolic arrays [42], which serve as accelerators for general matrix multiplication and convolution operations. These architectural advancements, aimed at improving accelerator flexibility and supporting real-time performance, are summarized in Table I. Therefore, the… view at source ↗
Figures from the paper (3 more)
Figure 4
Figure 4. Figure 4: Tree Diagram for Soft Real-Time Tasks Scheduling. [PITH_FULL_IMAGE:figures/full_fig_p007_4.png]
Figure 5
Figure 5. Figure 5: Tree Diagram for Hard Real-Time Tasks Scheduling. [PITH_FULL_IMAGE:figures/full_fig_p009_5.png]
Figure 6
Figure 6. Figure 6: Tree Diagram for Applications. process are leveraged to guide the redesign process, facilitating the development of a more efficient and systematic design methodology for AD systems. SCENIC [99] introduces a novel end-to-end co-design framework that integrates capabili…

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Reference graph

Works this paper leans on

126 extracted references · 73 canonical work pages · cited by 1 Pith paper

  1. [32]

    Gpusync: A framework for real-time gpu management

    Glenn A Elliott, Bryan C Ward, and James H Anderson. Gpusync: A framework for real-time gpu management. In2013 IEEE 34th Real- Time Systems Symposium, pages 33–44. IEEE, 2013

    2013

  2. [1]

    Extreme heterogeneity in deep learning architectures., 2019

    Jeff Anderson, Armin Mehrabian, Jiaxin Peng, and Tarek A El- Ghazawi. Extreme heterogeneity in deep learning architectures., 2019

    2019

  3. [2]

    Yolo9000: better, faster, stronger

    Joseph Redmon and Ali Farhadi. Yolo9000: better, faster, stronger. In Proceedings of the IEEE conference on computer vision and pattern recognition, pages 7263–7271, 2017

    2017

  4. [3]

    Gpu-accelerated real-time 3d track- ing for humanoid locomotion and stair climbing

    Philipp Michel, Joel Chestnutt, Satoshi Kagami, Koichi Nishiwaki, James Kuffner, and Takeo Kanade. Gpu-accelerated real-time 3d track- ing for humanoid locomotion and stair climbing. In2007 IEEE/RSJ International Conference on Intelligent Robots and Systems, pages 463–469. IEEE, 2007

    2007

  5. [4]

    Nvidia a100 gpu: Performance & innovation for gpu computing

    Jack Choquette and Wish Gandhi. Nvidia a100 gpu: Performance & innovation for gpu computing. In2020 IEEE Hot Chips 32 Symposium (HCS), pages 1–43. IEEE Computer Society, 2020

    2020

  6. [5]

    Xilinx ultrascale: The next-generation architecture for your next-generation architecture.Xilinx White Paper WP435, 143, 2013

    Steve Leibson and Nick Mehta. Xilinx ultrascale: The next-generation architecture for your next-generation architecture.Xilinx White Paper WP435, 143, 2013

    2013

  7. [6]

    Investigating ti keystone ii and quad-core arm cortex-a53 architectures for on-board space processing

    Benjamin Schwaller, Barath Ramesh, and Alan D George. Investigating ti keystone ii and quad-core arm cortex-a53 architectures for on-board space processing. In2017 IEEE High Performance Extreme Computing Conference (HPEC), pages 1–7. IEEE, 2017

    2017

  8. [7]

    Resource management challenges in the era of extreme heterogeneity

    Ronald B Brightwell. Resource management challenges in the era of extreme heterogeneity. Technical report, Sandia National Lab.(SNL- NM), Albuquerque, NM (United States), 2018

    2018

Show all 126 references
  1. [8]

    Many suspensions, many problems: a review of self-suspending tasks in real-time systems.Real- Time Systems, 55(1):144–207, 2019

    Jian-Jia Chen, Geoffrey Nelissen, Wen-Hung Huang, Maolin Yang, Bj¨orn Brandenburg, Konstantinos Bletsas, Cong Liu, Pascal Richard, Fr´ed´eric Ridouard, Neil Audsley, et al. Many suspensions, many problems: a review of self-suspending tasks in real-time systems.Real- Time Syste...

    2019

  2. [9]

    Self-suspension real-time tasks under fixed-relative-deadline fixed-priority scheduling

    Wen-Hung Huang and Jian-Jia Chen. Self-suspension real-time tasks under fixed-relative-deadline fixed-priority scheduling. In2016 Design, Automation & Test in Europe Conference & Exhibition (DATE), pages 1078–1083. IEEE, 2016

    2016

  3. [10]

    Analytical enhancements and practical insights for mpcp with self- suspensions

    Pratyush Patel, Iljoo Baek, Hyoseung Kim, and Ragunathan Rajkumar. Analytical enhancements and practical insights for mpcp with self- suspensions. In2018 IEEE Real-Time and Embedded Technology and Applications Symposium (RTAS), pages 177–189. IEEE, 2018

    2018

  4. [11]

    Stgm: Spatio-temporal gpu management for real-time tasks

    Sujan Kumar Saha, Yecheng Xiang, and Hyoseung Kim. Stgm: Spatio-temporal gpu management for real-time tasks. In2019 IEEE 25th International Conference on Embedded and Real-Time Computing Systems and Applications (RTCSA), pages 1–6. IEEE, 2019

    2019

  5. [12]

    Bal- ancing energy efficiency and real-time performance in gpu scheduling

    Yidi Wang, Mohsen Karimi, Yecheng Xiang, and Hyoseung Kim. Bal- ancing energy efficiency and real-time performance in gpu scheduling. In2021 IEEE Real-Time Systems Symposium (RTSS), pages 110–122. IEEE, 2021

    2021

  6. [13]

    Multi-core devices for safety-critical systems: A survey.ACM Computing Surveys (CSUR), 53(4):1–38, 2020

    Jon Perez Cerrolaza, Roman Obermaisser, Jaume Abella, Francisco J Cazorla, Kim Gr¨uttner, Irune Agirre, Hamidreza Ahmadian, and Imanol Allende. Multi-core devices for safety-critical systems: A survey.ACM Computing Surveys (CSUR), 53(4):1–38, 2020

    2020

  7. [14]

    A survey of hard real-time scheduling for multiprocessor systems.ACM computing surveys (CSUR), 43(4):1– 44, 2011

    Robert I Davis and Alan Burns. A survey of hard real-time scheduling for multiprocessor systems.ACM computing surveys (CSUR), 43(4):1– 44, 2011

    2011

  8. [15]

    A survey of timing verification techniques for multi-core real-time systems.ACM Computing Surveys (CSUR), 52(3):1–38, 2019

    Claire Maiza, Hamza Rihani, Juan M Rivas, Jo ¨el Goossens, Sebastian Altmeyer, and Robert I Davis. A survey of timing verification techniques for multi-core real-time systems.ACM Computing Surveys (CSUR), 52(3):1–38, 2019

    2019

  9. [16]

    A survey and comparative study of hard and soft real-time dynamic resource allocation strategies for multi-/many- core systems.ACM Computing Surveys (CSUR), 50(2):1–40, 2017

    Amit Kumar Singh, Piotr Dziurzanski, Hashan Roshantha Mendis, and Leandro Soares Indrusiak. A survey and comparative study of hard and soft real-time dynamic resource allocation strategies for multi-/many- core systems.ACM Computing Surveys (CSUR), 50(2):1–40, 2017

    2017

  10. [17]

    A survey of techniques for architecting and managing asymmetric multicore processors.ACM Computing Surveys (CSUR), 48(3):1–38, 2016

    Sparsh Mittal. A survey of techniques for architecting and managing asymmetric multicore processors.ACM Computing Surveys (CSUR), 48(3):1–38, 2016

    2016

  11. [18]

    Energy-aware scheduling for real-time systems: A survey.ACM Transactions on Embedded Computing Systems (TECS), 15(1):1–34, 2016

    Mario Bambagini, Mauro Marinoni, Hakan Aydin, and Giorgio But- tazzo. Energy-aware scheduling for real-time systems: A survey.ACM Transactions on Embedded Computing Systems (TECS), 15(1):1–34, 2016

    2016

  12. [19]

    Energy-efficient multicore scheduling for hard real-time systems: A survey.ACM Transactions on Embedded Computing Systems (TECS), 17(6):1–26, 2018

    Saad Zia Sheikh and Muhammad Adeel Pasha. Energy-efficient multicore scheduling for hard real-time systems: A survey.ACM Transactions on Embedded Computing Systems (TECS), 17(6):1–26, 2018

    2018

  13. [20]

    Limited preemp- tive scheduling for real-time systems

    Giorgio C Buttazzo, Marko Bertogna, and Gang Yao. Limited preemp- tive scheduling for real-time systems. a survey.IEEE transactions on Industrial Informatics, 9(1):3–15, 2012

    2012

  14. [21]

    The real-time linux kernel: A survey on preempt rt.ACM Computing Surveys (CSUR), 52(1):1–36, 2019

    Federico Reghenzani, Giuseppe Massari, and William Fornaciari. The real-time linux kernel: A survey on preempt rt.ACM Computing Surveys (CSUR), 52(1):1–36, 2019

    2019

  15. [22]

    Deep learning workload scheduling in gpu datacenters: A survey.ACM Comput

    Zhisheng Ye, Wei Gao, Qinghao Hu, Peng Sun, Xiaolin Wang, Yingwei Luo, Tianwei Zhang, and Yonggang Wen. Deep learning workload scheduling in gpu datacenters: A survey.ACM Comput. Surv., dec

  16. [23]

    Communication-efficient large-scale distributed deep learning: A comprehensive survey.arXiv preprint arXiv:2404.06114, 2024

    Feng Liang, Zhen Zhang, Haifeng Lu, Victor Leung, Yanyi Guo, and Xiping Hu. Communication-efficient large-scale distributed deep learning: A comprehensive survey.arXiv preprint arXiv:2404.06114, 2024

    2024 arXiv

  17. [24]

    Efficient training of large language models on distributed infrastructures: A survey.arXiv preprint arXiv:2407.20018, 2024

    Jiangfei Duan, Shuo Zhang, Zerui Wang, Lijuan Jiang, Wenwen Qu, Qinghao Hu, Guoteng Wang, Qizhen Weng, Hang Yan, Xingcheng Zhang, et al. Efficient training of large language models on distributed infrastructures: A survey.arXiv preprint arXiv:2407.20018, 2024

    2024 arXiv

  18. [25]

    Resource allocation and workload scheduling for large-scale distributed deep learning: A survey.arXiv preprint arXiv:2406.08115, 2024

    Feng Liang, Zhen Zhang, Haifeng Lu, Chengming Li, Victor Leung, Yanyi Guo, and Xiping Hu. Resource allocation and workload scheduling for large-scale distributed deep learning: A survey.arXiv preprint arXiv:2406.08115, 2024

    2024 arXiv

  19. [26]

    Gpu scheduling on the nvidia tx2: Hidden details revealed

    Tanya Amert, Nathan Otterness, Ming Yang, James H Anderson, and F Donelson Smith. Gpu scheduling on the nvidia tx2: Hidden details revealed. InReal-Time Systems Symposium. IEEE, 2017

    2017

  20. [27]

    An evaluation of the nvidia tx1 for supporting real-time computer-vision workloads

    Nathan Otterness, Ming Yang, Sarah Rust, Eunbyung Park, James H Anderson, F Donelson Smith, Alex Berg, and Shige Wang. An evaluation of the nvidia tx1 for supporting real-time computer-vision workloads. In2017 IEEE Real-Time and Embedded Technology and Applications Symposium (...

    2017

  21. [28]

    Execution time prediction for energy-efficient hardware accelerators

    Tao Chen, Alexander Rucker, and G Edward Suh. Execution time prediction for energy-efficient hardware accelerators. InProceedings of the 48th International Symposium on Microarchitecture, pages 457– 469, 2015

    2015

  22. [29]

    Making powerful enemies on nvidia gpus

    Tyler Yandrofski, Jingyuan Chen, Nathan Otterness, James H Ander- son, and F Donelson Smith. Making powerful enemies on nvidia gpus. In2022 IEEE Real-Time Systems Symposium (RTSS), pages 383–395. IEEE, 2022

    2022

  23. [30]

    Demystifying nvidia gpu internals to enable reliable gpu management

    Joshua Bakita and James H Anderson. Demystifying nvidia gpu internals to enable reliable gpu management. In2024 IEEE 30th Real- Time and Embedded Technology and Applications Symposium (RTAS), pages 294–305. IEEE, 2024

    2024

  24. [31]

    Estimating the wcet of gpu- accelerated applications using hybrid analysis

    Adam Betts and Alastair Donaldson. Estimating the wcet of gpu- accelerated applications using hybrid analysis. In2013 25th Euromicro Conference on Real-Time Systems, pages 193–202. IEEE, 2013

    2013

  25. [33]

    Chimera: Collaborative preemption for multitasking on a shared gpu.ACM SIGARCH Computer Architecture News, 43(1):593–606, 2015

    Jason Jong Kyu Park, Yongjun Park, and Scott Mahlke. Chimera: Collaborative preemption for multitasking on a shared gpu.ACM SIGARCH Computer Architecture News, 43(1):593–606, 2015

    2015

  26. [34]

    Unleashing the power of preemptive priority-based scheduling for real-time gpu tasks.arXiv preprint arXiv:2401.16529, 2024

    Yidi Wang, Cong Liu, Daniel Wong, and Hyoseung Kim. Unleashing the power of preemptive priority-based scheduling for real-time gpu tasks.arXiv preprint arXiv:2401.16529, 2024

    2024 arXiv

  27. [35]

    Supporting preemptive task exe- cutions and memory copies in gpgpus

    Can Basaran and Kyoung-Don Kang. Supporting preemptive task exe- cutions and memory copies in gpgpus. In24th Euromicro Conference on Real-Time Systems (ECRTS 2012). IEEE, 2012. 19

    2012

  28. [36]

    Enabling preemptive multiprogramming on gpus

    Ivan Tanasic, Isaac Gelado, Javier Cabezas, Alex Ramirez, Nacho Navarro, and Mateo Valero. Enabling preemptive multiprogramming on gpus. InComputer Architecture (ISCA), 2014 ACM/IEEE 41st International Symposium on, pages 193–204. IEEE, 2014

    2014

  29. [37]

    GCAPS: GPU Context-Aware Preemptive Priority-Based Scheduling for Real- Time Tasks

    Yidi Wang, Cong Liu, Daniel Wong, and Hyoseung Kim. GCAPS: GPU Context-Aware Preemptive Priority-Based Scheduling for Real- Time Tasks. In Rodolfo Pellizzoni, editor,36th Euromicro Confer- ence on Real-Time Systems (ECRTS 2024), volume 298 ofLeibniz International Proceedings i...

    2024

  30. [38]

    Nvidia a100 tensor core gpu: Performance and innovation.IEEE Micro, 41(2):29–35, 2021

    Jack Choquette, Wishwesh Gandhi, Olivier Giroux, Nick Stam, and Ronny Krashinsky. Nvidia a100 tensor core gpu: Performance and innovation.IEEE Micro, 41(2):29–35, 2021

    2021

  31. [39]

    Anderson

    Joshua Bakita and James H. Anderson. Hardware compute partitioning on NVIDIA gpus. In29th IEEE Real-Time and Embedded Technology and Applications Symposium, RTAS 2023, San Antonio, TX, USA, May 9-12, 2023, pages 54–66. IEEE, 2023

    2023

  32. [40]

    Spet: Transparent sram allocation and model partitioning for real-time dnn tasks on edge tpu

    Changhun Han, Hoon Sung Chwa, Kilho Lee, and Sangeun Oh. Spet: Transparent sram allocation and model partitioning for real-time dnn tasks on edge tpu. In2023 60th ACM/IEEE Design Automation Conference (DAC), pages 1–6, 2023

    2023

  33. [41]

    Prema: A predictive multi-task scheduling algorithm for preemptible neural processing units

    Yujeong Choi and Minsoo Rhu. Prema: A predictive multi-task scheduling algorithm for preemptible neural processing units. In 2020 IEEE International Symposium on High Performance Computer Architecture (HPCA), pages 220–233, 2020

    2020

  34. [42]

    Enabling fine-grained spatial multitasking on systolic-array npus using dataflow mirroring.IEEE Transactions on Computers, 2023

    Jinwoo Choi, Yeonan Ha, Jounghoo Lee, Sangsu Lee, Jinho Lee, Hanhwi Jang, and Youngsok Kim. Enabling fine-grained spatial multitasking on systolic-array npus using dataflow mirroring.IEEE Transactions on Computers, 2023

    2023

  35. [43]

    Dynamic resource partitioning for multi-tenant systolic array based dnn accelerator

    Midia Reshadi and David Gregg. Dynamic resource partitioning for multi-tenant systolic array based dnn accelerator. In2023 31st Euromi- cro International Conference on Parallel, Distributed and Network- Based Processing (PDP), pages 76–83. IEEE, 2023

    2023

  36. [44]

    A framework for supporting real-time applications on dynamic reconfigurable fpgas

    Alessandro Biondi, Alessio Balsini, Marco Pagani, Enrico Rossi, Mauro Marinoni, and Giorgio Buttazzo. A framework for supporting real-time applications on dynamic reconfigurable fpgas. In2016 IEEE Real-Time Systems Symposium (RTSS), pages 1–12, 2016

    2016

  37. [45]

    Roberto Cordone, Francesco Redaelli, Massimo Antonio Redaelli, Marco Domenico Santambrogio, and Donatella Sciuto. Partitioning and scheduling of task graphs on partially dynamically reconfigurable fpgas.IEEE Transactions on Computer-Aided Design of Integrated Circuits and Syst...

    2009

  38. [46]

    A memory scheduling infrastructure for multi-core systems with re-programmable logic

    Denis Hoornaert, Shahin Roozkhosh, and Renato Mancuso. A memory scheduling infrastructure for multi-core systems with re-programmable logic. In33rd Euromicro Conference on Real-Time Systems (ECRTS 2021), pages 2–1. Schloss Dagstuhl–Leibniz-Zentrum f ¨ur Informatik, 2021

    2021

  39. [47]

    The potential of pro- grammable logic in the middle: Cache bleaching

    Shahin Roozkhosh and Renato Mancuso. The potential of pro- grammable logic in the middle: Cache bleaching. In2020 IEEE Real- Time and Embedded Technology and Applications Symposium (RTAS), pages 296–309. IEEE, 2020

    2020

  40. [48]

    Gpes: A preemptive execution system for gpgpu computing

    Husheng Zhou, Guangmo Tong, and Cong Liu. Gpes: A preemptive execution system for gpgpu computing. InReal-Time and Embedded Technology and Applications Symposium (RTAS). IEEE, 2015

    2015

  41. [49]

    Effisha: A software framework for enabling effficient preemptive scheduling of gpu

    Guoyang Chen, Yue Zhao, Xipeng Shen, and Huiyang Zhou. Effisha: A software framework for enabling effficient preemptive scheduling of gpu. InProceedings of the 22nd ACM SIGPLAN Symposium on Principles and Practice of Parallel Programming, 2017

    2017

  42. [50]

    Programming abstractions for preemptive scheduling in fpgas using partial reconfiguration.arXiv preprint arXiv:2209.04410, 2022

    Gabriel Rodriguez-Canal, Nick Brown, Yuri Torres, and Arturo Gonzalez-Escribano. Programming abstractions for preemptive scheduling in fpgas using partial reconfiguration.arXiv preprint arXiv:2209.04410, 2022

    2022 arXiv

  43. [51]

    Improving gpu throughput of reservoir simulations using nvidia mps and mig

    R Gandham, Y Zhang, K Esler, and V Natoli. Improving gpu throughput of reservoir simulations using nvidia mps and mig. In Fifth EAGE Workshop on High Performance Computing for Upstream, volume 2021, pages 1–5. European Association of Geoscientists & Engineers, 2021

    2021

  44. [52]

    worst practices

    Nathan Otterness and James H Anderson. Exploring AMD GPU scheduling details by experimenting with “worst practices”,”. InPro- ceedings of the 29th International Conference on Real-Time Networks and Systems, 2021

    2021

  45. [53]

    AMD GPUs as an alternative to nvidia for supporting real-time workloads

    Nathan Otterness and James H Anderson. AMD GPUs as an alternative to nvidia for supporting real-time workloads. In32nd Euromicro Conference on Real-Time Systems (ECRTS 2020). Schloss Dagstuhl- Leibniz-Zentrum f ¨ur Informatik, 2020

    2020

  46. [54]

    Sˆ 3dnn: Supervised streaming and scheduling for gpu-accelerated real-time dnn workloads

    Husheng Zhou, Soroush Bateni, and Cong Liu. Sˆ 3dnn: Supervised streaming and scheduling for gpu-accelerated real-time dnn workloads. In2018 IEEE Real-Time and Embedded Technology and Applications Symposium (RTAS), pages 190–201. IEEE, 2018

    2018

  47. [55]

    Efficient cuda stream management for multi-dnn real- time inference on embedded gpus.Journal of Systems Architecture, 139:102888, 2023

    Weiguang Pang, Xiantong Luo, Kailun Chen, Dong Ji, Lei Qiao, and Wang Yi. Efficient cuda stream management for multi-dnn real- time inference on embedded gpus.Journal of Systems Architecture, 139:102888, 2023

    2023

  48. [56]

    Fixed-relative-deadline scheduling of hard real-time tasks with self-suspensions

    Jian-Jia Chen and Cong Liu. Fixed-relative-deadline scheduling of hard real-time tasks with self-suspensions. In2014 IEEE Real-Time Systems Symposium, pages 149–160, 2014

    2014

  49. [57]

    Edf-like scheduling for self-suspending real-time tasks

    Mario G ¨unzel, Georg von der Br ¨uggen, Kuan-Hsun Chen, and Jian-Jia Chen. Edf-like scheduling for self-suspending real-time tasks. In2022 IEEE Real-Time Systems Symposium (RTSS), pages 172–184, 2022

    2022

  50. [58]

    Rtgpu: Real- time gpu scheduling of hard deadline parallel tasks with fine-grain utilization.arXiv preprint arXiv:2101.10463, 2021

    An Zou, Jing Li, Christopher D Gill, and Xuan Zhang. Rtgpu: Real- time gpu scheduling of hard deadline parallel tasks with fine-grain utilization.arXiv preprint arXiv:2101.10463, 2021

    2021 arXiv

  51. [59]

    Shape: Scheduling of fixed-priority tasks on heterogeneous architectures with multiple cpus and many pes

    Yuankai Xu, Tiancheng He, Ruiqi Sun, Yehan Ma, Yier Jin, and An Zou. Shape: Scheduling of fixed-priority tasks on heterogeneous architectures with multiple cpus and many pes. InProceedings of the 41st IEEE/ACM International Conference on Computer-Aided Design, pages 1–9, 2022

    2022

  52. [60]

    Formal analysis of timing diversity for autonomous systems

    Anika Christmann, Robin Hapka, and Rolf Ernst. Formal analysis of timing diversity for autonomous systems. In2023 Design, Automation & Test in Europe Conference & Exhibition (DATE), pages 1–6. IEEE, 2023

    2023

  53. [61]

    Controlling high- performance platform uncertainties with timing diversity

    Robin Hapka, Anika Christmann, and Rolf Ernst. Controlling high- performance platform uncertainties with timing diversity. In2022 IEEE 28th International Conference on Embedded and Real-Time Computing Systems and Applications (RTCSA), pages 212–219. IEEE, 2022

    2022

  54. [62]

    A survey on real-time dag scheduling, revisiting the global-partitioned infinity war.Real-Time Systems, 59(3):479–530, 2023

    Micaela Verucchi, Ignacio Sa ˜nudo Olmedo, and Marko Bertogna. A survey on real-time dag scheduling, revisiting the global-partitioned infinity war.Real-Time Systems, 59(3):479–530, 2023

    2023

  55. [63]

    The hpc-dag task model for heterogeneous real-time systems.IEEE Transactions on Computers, 70(10):1747–1761, 2021

    Zahaf Houssam-Eddine, Nicola Capodieci, Roberto Cavicchioli, Giuseppe Lipari, and Marko Bertogna. The hpc-dag task model for heterogeneous real-time systems.IEEE Transactions on Computers, 70(10):1747–1761, 2021

    2021

  56. [64]

    Path-level network transformation for efficient architecture search

    Han Cai, Jiacheng Yang, Weinan Zhang, Song Han, and Yong Yu. Path-level network transformation for efficient architecture search. InProceedings of the 35th International Conference on Machine Learning, volume 80 ofPMLR, pages 678–687, 2018

    2018

  57. [65]

    Gomez, Łukasz Kaiser, and Illia Polosukhin

    Ashish Vaswani, Noam Shazeer, Niki Parmar, Jakob Uszkoreit, Llion Jones, Aidan N. Gomez, Łukasz Kaiser, and Illia Polosukhin. Attention is all you need. InProceedings of the 31st International Conference on Neural Information Processing Systems, NIPS’17, page 6000–6010, Red Ho...

    2017

  58. [66]

    Response-time analysis of ros 2 processing chains under reservation- based scheduling

    Daniel Casini, Tobias Blaß, Ingo L ¨utkebohle, and Bj ¨orn Brandenburg. Response-time analysis of ros 2 processing chains under reservation- based scheduling. In31st Euromicro Conference on Real-Time Systems, pages 1–23. Schloss Dagstuhl, 2019

    2019

  59. [67]

    Response time analysis and priority assignment of processing chains on ros2 executors

    Yue Tang, Zhiwei Feng, Nan Guan, Xu Jiang, Mingsong Lv, Qingxu Deng, and Wang Yi. Response time analysis and priority assignment of processing chains on ros2 executors. In2020 IEEE Real-Time Systems Symposium (RTSS), pages 231–243. IEEE, 2020

    2020

  60. [68]

    Picas: New design of priority-driven chain-aware scheduling for ros2

    Hyunjong Choi, Yecheng Xiang, and Hyoseung Kim. Picas: New design of priority-driven chain-aware scheduling for ros2. In2021 IEEE 27th Real-Time and Embedded Technology and Applications Symposium (RTAS), pages 251–263. IEEE, 2021

    2021

  61. [69]

    Direct rendering manager (drm) memory management, 2024

    The kernel development community. Direct rendering manager (drm) memory management, 2024

    2024

  62. [70]

    NVIDIA Corporation.NVIDIA Multi-Process Service (MPS) Docu- mentation, 2024

    2024

  63. [71]

    Nvidia multi-instance gpu

    NVIDIA Corporation. Nvidia multi-instance gpu. https://www.nvidia.com/en-us/technologies/multi-instance-gpu/, 2023

    2023

  64. [72]

    Carss: Client-aware resource sharing and scheduling for heterogeneous applications

    Iljoo Baek, Matthew Harding, Akshit Kanda, Kyung Ryeol Choi, Soheil Samii, and Ragunathan Raj Rajkumar. Carss: Client-aware resource sharing and scheduling for heterogeneous applications. In2020 IEEE Real-Time and Embedded Technology and Applications Symposium (RTAS), pages 32...

    2020

  65. [73]

    Timegraph: Gpu scheduling for real-time multi-tasking environments

    Shinpei Kato, Karthik Lakshmanan, Raj Rajkumar, and Yutaka Ishikawa. Timegraph: Gpu scheduling for real-time multi-tasking environments. InProc. USENIX ATC, pages 17–30, 2011

    2011

  66. [74]

    Anytime algorithms for gpu architectures

    Rahul Mangharam and Aminreza Abrahimi Saba. Anytime algorithms for gpu architectures. In2011 IEEE 32nd Real-Time Systems Sympo- sium, pages 47–56, 2011

    2011

  67. [75]

    Donelson Smith, and James H

    Tanya Amert, Zelin Tong, Sergey V oronov, Joshua Bakita, F. Donelson Smith, and James H. Anderson. Timewall: Enabling time partitioning 20 for real-time multicore+accelerator platforms. In2021 IEEE Real-Time Systems Symposium (RTSS), pages 455–468, 2021

    2021

  68. [76]

    Globally scheduled real-time multiprocessor systems with gpus.Real-Time Systems, 48(1):34–74, 2012

    Glenn A Elliott and James H Anderson. Globally scheduled real-time multiprocessor systems with gpus.Real-Time Systems, 48(1):34–74, 2012

    2012

  69. [77]

    Elliott and James H

    Glenn A. Elliott and James H. Anderson. An optimal k-exclusion real- time locking protocol motivated by multi-gpu systems.Real-Time Syst., 49(2):140–170, March 2013

    2013

  70. [78]

    Dream: A dynamic scheduler for dynamic real-time multi-model ml workloads

    Seah Kim, Hyoukjun Kwon, Jinook Song, Jihyuck Jo, Yu-Hsin Chen, Liangzhen Lai, and Vikas Chandra. Dream: A dynamic scheduler for dynamic real-time multi-model ml workloads. InProceedings of the 28th ACM International Conference on Architectural Support for Pro- gramming Langua...

    2024

  71. [79]

    Pegasus: coordinated scheduling for virtu- alized accelerator-based systems

    Vishakha Gupta, Karsten Schwan, Niraj Tolia, Vanish Talwar, and Parthasarathy Ranganathan. Pegasus: coordinated scheduling for virtu- alized accelerator-based systems. InProceedings of the 2011 USENIX Conference on USENIX Annual Technical Conference, USENIX- ATC’11, page 3, US...

    2011

  72. [80]

    Towards energy- efficient real-time scheduling of heterogeneous multi-gpu systems

    Yidi Wang, Mohsen Karimi, and Hyoseung Kim. Towards energy- efficient real-time scheduling of heterogeneous multi-gpu systems. In 2022 IEEE Real-Time Systems Symposium (RTSS), pages 409–421. IEEE, 2022

    2022

  73. [81]

    Srijeeta Maity, Rudrajyoti Roy, Anirban Majumder, Soumyajit Dey, and Ashish R. Hota. Future aware dynamic thermal management in cpu- gpu embedded platforms. In2022 IEEE Real-Time Systems Symposium (RTSS), pages 396–408, 2022

    2022

  74. [82]

    Therma-mics: Thermal-aware scheduling for fault-tolerant mixed-criticality systems.IEEE Trans- actions on Parallel and Distributed Systems, 33(7):1678–1694, 2022

    Sepideh Safari, Heba Khdr, Pourya Gohari-Nazari, Mohsen Ansari, Shaahin Hessabi, and J ¨org Henkel. Therma-mics: Thermal-aware scheduling for fault-tolerant mixed-criticality systems.IEEE Trans- actions on Parallel and Distributed Systems, 33(7):1678–1694, 2022

    2022

  75. [83]

    Nvidia system task manager (stm)

    NVIDIA Corporation. Nvidia system task manager (stm). https://developer.nvidia.com/docs/drive/drive-os/6.0.10/public/ driveworks-stm/nvstm html/index.html, 2024. Accessed: 2024-12-23

    2024

  76. [84]

    Enable intel tcc in slim bootloader

    Intel Corporation. Enable intel tcc in slim bootloader. https: //slimbootloader.github.io/how-tos/enable-intel-tcc.html, 2024. Ac- cessed: 2024-12-20

    2024

  77. [85]

    Schedulability and priority assignment for multi-segment self-suspending real-time tasks under fixed-priority scheduling

    Wen-Hung Huang and Jian-Jia Chen. Schedulability and priority assignment for multi-segment self-suspending real-time tasks under fixed-priority scheduling. InTechnical report. Technical University of Dortmund, 2015

    2015

  78. [86]

    A gpu kernel transaction- ization scheme for preemptive priority scheduling

    Hyeonsu Lee, Jaehun Roh, and Euiseong Seo. A gpu kernel transaction- ization scheme for preemptive priority scheduling. In2018 IEEE Real- Time and Embedded Technology and Applications Symposium (RTAS), pages 202–213. IEEE, 2018

    2018

  79. [87]

    A framework for supporting real-time applications on dynamic reconfigurable fpgas

    Alessandro Biondi, Alessio Balsini, Marco Pagani, Enrico Rossi, Mauro Marinoni, and Giorgio Buttazzo. A framework for supporting real-time applications on dynamic reconfigurable fpgas. In2016 IEEE Real-Time Systems Symposium (RTSS), pages 1–12. IEEE, 2016

    2016

  80. [88]

    Deadline-based scheduling for gpu with preemption support

    Nicola Capodieci, Roberto Cavicchioli, Marko Bertogna, and Aingara Paramakuru. Deadline-based scheduling for gpu with preemption support. In2018 IEEE Real-Time Systems Symposium (RTSS), pages 119–130. IEEE, 2018

    2018

  81. [89]

    Hardware acceleration with zero-copy memory management for heterogeneous computing

    Oren Bell, Chris Gill, and Xuan Zhang. Hardware acceleration with zero-copy memory management for heterogeneous computing. In 2023 IEEE 29th International Conference on Embedded and Real-Time Computing Systems and Applications (RTCSA), pages 28–37. IEEE, 2023

    2023

  82. [90]

    Heterogeneous quasi-partitioned scheduling

    Ernesto Massa, George Lima, Bjorn Andersson, and Vinicius Petrucci. Heterogeneous quasi-partitioned scheduling. In2021 IEEE Real-Time Systems Symposium (RTSS), pages 266–278. IEEE, 2021

    2021

  83. [91]

    Pipelined data-parallel cpu/gpu scheduling for multi-dnn real-time inference

    Yecheng Xiang and Hyoseung Kim. Pipelined data-parallel cpu/gpu scheduling for multi-dnn real-time inference. In2019 IEEE Real-Time Systems Symposium (RTSS), pages 392–405. IEEE, 2019

    2019

  84. [92]

    Pred- joule: A timing-predictable energy optimization framework for deep neural networks

    Soroush Bateni, Husheng Zhou, Yuankun Zhu, and Cong Liu. Pred- joule: A timing-predictable energy optimization framework for deep neural networks. In2018 IEEE Real-Time Systems Symposium (RTSS), pages 107–118. IEEE, 2018

    2018

  85. [93]

    Thermal-aware scheduling for integrated cpus–gpu platforms.ACM Transactions on Embedded Computing Systems (TECS), 18(5s):1–25, 2019

    Youngmoon Lee, Kang G Shin, and Hoon Sung Chwa. Thermal-aware scheduling for integrated cpus–gpu platforms.ACM Transactions on Embedded Computing Systems (TECS), 18(5s):1–25, 2019

    2019

  86. [94]

    Thermal-aware servers for real-time tasks on multi-core gpu-integrated embedded systems

    Seyedmehdi Hosseinimotlagh and Hyoseung Kim. Thermal-aware servers for real-time tasks on multi-core gpu-integrated embedded systems. In2019 IEEE Real-Time and Embedded Technology and Applications Symposium (RTAS), pages 254–266. IEEE, 2019

    2019

  87. [95]

    Dymaxion: Optimizing memory access patterns for heterogeneous systems

    Shuai Che, Jeremy W Sheaffer, and Kevin Skadron. Dymaxion: Optimizing memory access patterns for heterogeneous systems. In Proceedings of 2011 international conference for high performance computing, networking, storage and analysis, pages 1–11, 2011

    2011

  88. [96]

    R ˆ3: On-device real-time deep reinforcement learning for autonomous robotics

    Zexin Li, Aritra Samanta, Yufei Li, Andrea Soltoggio, Hyoseung Kim, and Cong Liu. R ˆ3: On-device real-time deep reinforcement learning for autonomous robotics. In2023 IEEE Real-Time Systems Symposium (RTSS), pages 131–144. IEEE, 2023

    2023

  89. [97]

    Prophet: Realizing a predictable real-time perception pipeline for autonomous vehicles

    Liangkai Liu, Zheng Dong, Yanzhi Wang, and Weisong Shi. Prophet: Realizing a predictable real-time perception pipeline for autonomous vehicles. In2022 IEEE Real-Time Systems Symposium (RTSS), pages 305–317. IEEE, 2022

    2022

  90. [98]

    Real-time scheduling of autonomous driving system with guaranteed timing correctness

    Jinghao Sun, Kailu Duan, Xisheng Li, Nan Guan, Zhishan Guo, Qingxu Deng, and Guozhen Tan. Real-time scheduling of autonomous driving system with guaranteed timing correctness. In2023 IEEE 29th Real- Time and Embedded Technology and Applications Symposium (RTAS), pages 185–197, 2023

    2023

  91. [99]

    Scenic: Capability and scheduling co-design for intelligent controller on heterogeneous platforms

    Jintao Chen, An Zou, Yuankai Xu, and Yehan Ma. Scenic: Capability and scheduling co-design for intelligent controller on heterogeneous platforms. In2024 IEEE Real-Time Systems Symposium (RTSS), pages 201–214. IEEE, 2024

    2024

  92. [100]

    Red: A systematic real- time scheduling approach for robotic environmental dynamics

    Zexin Li, Tao Ren, Xiaoxi He, and Cong Liu. Red: A systematic real- time scheduling approach for robotic environmental dynamics. In2023 IEEE Real-Time Systems Symposium (RTSS), pages 210–223. IEEE, 2023

    2023

  93. [101]

    Rosgm: A real-time gpu management framework with plug-in policies for ros 2

    Ruoxiang Li, Tao Hu, Xu Jiang, Laiwen Li, Wenxuan Xing, Qingxu Deng, and Nan Guan. Rosgm: A real-time gpu management framework with plug-in policies for ros 2. In2023 IEEE 29th Real-Time and Embedded Technology and Applications Symposium (RTAS), pages 93– 105, 2023

    2023

  94. [102]

    Paam: A framework for coordinated and priority-driven accelerator management in ros 2

    Daniel Enright, Yecheng Xiang, Hyunjong Choi, and Hyoseung Kim. Paam: A framework for coordinated and priority-driven accelerator management in ros 2. In2024 IEEE 30th Real-Time and Embedded Technology and Applications Symposium (RTAS), pages 81–94, 2024

    2024

  95. [103]

    RTScale: Sensitivity-Aware Adaptive Image Scaling for Real-Time Object De- tection

    Seonyeong Heo, Shinnung Jeong, and Hanjun Kim. RTScale: Sensitivity-Aware Adaptive Image Scaling for Real-Time Object De- tection. In Martina Maggio, editor,34th Euromicro Conference on Real-Time Systems (ECRTS 2022), volume 231 ofLeibniz Interna- tional Proceedings in Informa...

    2022

  96. [104]

    Rt-mot: Confidence-aware real-time scheduling framework for multi-object tracking tasks

    Donghwa Kang, Seunghoon Lee, Hoon Sung Chwa, Seung-Hwan Bae, Chang Mook Kang, Jinkyu Lee, and Hyeongboo Baek. Rt-mot: Confidence-aware real-time scheduling framework for multi-object tracking tasks. In2022 IEEE Real-Time Systems Symposium (RTSS), pages 318–330, 2022

    2022

  97. [105]

    Shin, and Hoon Sung Chwa

    Woosung Kang, Siwoo Chung, Jeremy Yuhyun Kim, Youngmoon Lee, Kilho Lee, Jinkyu Lee, Kang G. Shin, and Hoon Sung Chwa. Dnn-sam: Split-and-merge dnn execution for real-time object detection. In2022 IEEE 28th Real-Time and Embedded Technology and Applications Symposium (RTAS), pa...

    2022

  98. [106]

    R-tod: Real-time object detector with minimized end-to- end delay for autonomous driving

    Wonseok Jang, Hansaem Jeong, Kyungtae Kang, Nikil Dutt, and Jong- Chan Kim. R-tod: Real-time object detector with minimized end-to- end delay for autonomous driving. In2020 IEEE Real-Time Systems Symposium (RTSS), pages 191–204, 2020

    2020

  99. [107]

    Self-cueing real-time attention scheduling in criticality-aware visual machine perception

    Shengzhong Liu, Xinzhe Fu, Maggie Wigness, Philip David, Shuochao Yao, Lui Sha, and Tarek Abdelzaher. Self-cueing real-time attention scheduling in criticality-aware visual machine perception. In2022 IEEE 28th Real-Time and Embedded Technology and Applications Symposium (RTAS)...

    2022

  100. [108]

    Mobipose: Real-time multi- person pose estimation on mobile devices

    Jinrui Zhang, Deyu Zhang, Xiaohui Xu, Fucheng Jia, Yunxin Liu, Xuanzhe Liu, Ju Ren, and Yaoxue Zhang. Mobipose: Real-time multi- person pose estimation on mobile devices. InProceedings of the 18th Conference on Embedded Networked Sensor Systems, pages 136–149, 2020

    2020

  101. [109]

    Jigsaw: Taming bev- centric perception on dual-soc for autonomous driving

    Lingyu Sun, Chao Li, Tianhao Huang, Cheng Xu, Xinkai Wang, Bingchuan Sun, Shibo Rui, and Minyi Guo. Jigsaw: Taming bev- centric perception on dual-soc for autonomous driving. In2024 IEEE Real-Time Systems Symposium (RTSS), pages 280–293. IEEE, 2024

    2024

  102. [110]

    Flex: Adaptive task batch scheduling with elastic fusion in multi-modal multi-view machine perception

    Yuhang Xu, Zixuan Liu, Xinzhe Fu, Shengzhong Liu, Fan Wu, and Guihai Chen. Flex: Adaptive task batch scheduling with elastic fusion in multi-modal multi-view machine perception. In2024 IEEE Real- Time Systems Symposium (RTSS), pages 294–307. IEEE, 2024

    2024

  103. [111]

    Rt-lm: Uncertainty-aware resource management for real-time inference of language models

    Yufei Li, Zexin Li, Wei Yang, and Cong Liu. Rt-lm: Uncertainty-aware resource management for real-time inference of language models. In 2023 IEEE Real-Time Systems Symposium (RTSS), pages 158–171, 2023. 21

    2023

  104. [112]

    Llumnix: Dynamic scheduling for large language model serving

    Biao Sun, Ziming Huang, Hanyu Zhao, Wencong Xiao, Xinyi Zhang, Yong Li, and Wei Lin. Llumnix: Dynamic scheduling for large language model serving. In18th USENIX Symposium on Operating Systems Design and Implementation (OSDI 24), pages 173–191, Santa Clara, CA, July 2024. USENI...

    2024

  105. [113]

    Exploring real-time satellite computing: From energy and thermal perspectives

    Qing Li, Shangguang Wang, Chenren Xu, Xiao Ma, Mengwei Xu, Ao Zhou, Ruolin Xing, Boyuan Yang, Zuo Zhu, Ying Zhang, and Xuanzhe Liu. Exploring real-time satellite computing: From energy and thermal perspectives. In2024 IEEE Real-Time Systems Symposium (RTSS), pages 161–173. IEEE, 2024

    2024

  106. [114]

    Boxr: Body and head motion optimization framework for extended reality

    Ziliang Zhang, Zexin Li, Hyoseung Kim, and Cong Liu. Boxr: Body and head motion optimization framework for extended reality. In2024 IEEE Real-Time Systems Symposium (RTSS), pages 70–82. IEEE, 2024

    2024

  107. [115]

    Heimdall: mobile gpu coordination platform for augmented reality applications

    Juheon Yi and Youngki Lee. Heimdall: mobile gpu coordination platform for augmented reality applications. InProceedings of the 26th Annual International Conference on Mobile Computing and Networking, pages 1–14, 2020

    2020

  108. [116]

    Pimgcn: A reram-based pim design for graph convolutional network acceleration

    Tao Yang, Dongyue Li, Yibo Han, Yilong Zhao, Fangxin Liu, Xiaoyao Liang, Zhezhi He, and Li Jiang. Pimgcn: A reram-based pim design for graph convolutional network acceleration. In2021 58th ACM/IEEE Design Automation Conference (DAC), pages 583–588, 2021

    2021

  109. [117]

    Biswadip Maity, Saehanseul Yi, Dongjoo Seo, Leming Cheng, Sung- Soo Lim, Jong-Chan Kim, Bryan Donyanavard, and Nikil Dutt. Chauf- feur: Benchmark suite for design and end-to-end analysis of self-driving vehicles on embedded systems.ACM Transactions on Embedded Computing System...

    2021

  110. [118]

    EyeQ Chip Technology

    Mobileye. EyeQ Chip Technology. https://www.mobileye.com/ technology/eyeq-chip/, 2023

    2023

  111. [119]

    Rama Venkatasubramanian, Don Steiss, Greg Shurtz, Tim Ander- son, Kai Chirca, Raghavendra Santhanagopal, Niraj Nandan, Anish Reghunath, Hetul Sanghvi, Daniel Wu, et al. 2.6 a 16nm 3.5 b+ transistor¿ 14tops 2-to-10w multicore soc platform for automotive and embedded application...

    2020

  112. [120]

    Image processing with cuda c++

    Axel Mendoza. Image processing with cuda c++. https://github.com/ ConsciousML/img-processing-cuda, 2020

    2020

  113. [121]

    Fast cuda kernels for resnet inference

    Qiantong Xu. Fast cuda kernels for resnet inference. https://github. com/xuqiantong/CUDA-Winograd, 2019

    2019

  114. [122]

    Rnn-transducer prefix beam search

    Janvijay Singh. Rnn-transducer prefix beam search. https://github.com/ iamjanvijay/rnnt decoder cuda, 2020

    2020

  115. [123]

    Samples for cuda developers which demonstrates features in cuda toolkit

    Mahesh Doijade, Rutwik Choughule, and Rob Nertney. Samples for cuda developers which demonstrates features in cuda toolkit. https: //github.com/NVIDIA/cuda-samples/tree/master/Samples, 2022

    2022

  116. [124]

    An investigation of unified memory access performance in cuda

    Raphael Landaverde, Tiansheng Zhang, Ayse K Coskun, and Martin Herbordt. An investigation of unified memory access performance in cuda. In2014 IEEE High Performance Extreme Computing Conference (HPEC), pages 1–6. IEEE, 2014. An Zou(Senior Member, IEEE) is an Associate Professo...

    2014

  117. [2013]

    She received the TASE 2024 Best Application Paper Award

    Her work has been published at top-tier conferences and journals, such as RTSS, RTAS, EMSOFT, ICCPS, and TCAD. She received the TASE 2024 Best Application Paper Award. She serves as the TPC of RTSS, RTAS, ECRTS, and SECON. Jing Liis an Associate Professor in the Department of ...

    2024

  118. [2019]

    He received Best Paper Award at DAC’15, ASP-DAC’16, HOST’17, ACM TODAES’18, GLSVLSI’18, and DATE’19

Pith tools

Reviewed August 15, 2026 · model on record in the stance chip above.