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Publications (10 of 35) Show all publications
Zhang, Y.-W., Ma, J.-P., Zheng, H. & Gu, Z. (2024). Criticality-aware EDF scheduling for constrained-deadline imprecise mixed-criticality systems. IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems, 43(2), 480-491
Open this publication in new window or tab >>Criticality-aware EDF scheduling for constrained-deadline imprecise mixed-criticality systems
2024 (English)In: IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems, ISSN 0278-0070, E-ISSN 1937-4151, Vol. 43, no 2, p. 480-491Article in journal (Refereed) Published
Abstract [en]

EDF-VD first focuses on the classic mixed-criticality task model in which all low criticality (LO) tasks are abandoned in the high criticality mode, which is an effective dynamic priority scheduling algorithm for mixed-criticality systems. However, it has low schedulability for the imprecise mixed-criticality (IMC) task model with constrained-deadlines, in which LO tasks are provided graceful degradation services instead of being abandoned. In this paper, we study how to improve schedulability for the IMC tasks model. First, we propose a novel criticality-aware EDF scheduling algorithm (CA-EDF) that tries to delay the LO task execution to improve schedulability. Second, we derive sufficient conditions of schedulability for CA-EDF based on the Demand Bound Function. Finally, we evaluate CA-EDF through extensive simulation. The experimental results indicate that CA-EDF can improve the schedulability ratio by about 13.10% compared to the existing algorithms.

Place, publisher, year, edition, pages
Institute of Electrical and Electronics Engineers (IEEE), 2024
Keywords
Computational modeling, demand bound function, graceful degradation, imprecise mixed-criticality, Industries, Job shop scheduling, Program processors, real-time scheduling, Scheduling algorithms, Switches, Task analysis
National Category
Computer Engineering Computer Sciences
Identifiers
urn:nbn:se:umu:diva-219518 (URN)10.1109/TCAD.2023.3318512 (DOI)2-s2.0-85181578343 (Scopus ID)
Funder
The Kempe Foundations
Available from: 2024-01-22 Created: 2024-01-22 Last updated: 2024-01-22Bibliographically approved
Feng, Z., Wu, C., Deng, Q., Lin, Y., Gao, S. & Gu, Z. (2024). On the scheduling of fault-tolerant time-sensitive networking with IEEE 802.1CB. IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems
Open this publication in new window or tab >>On the scheduling of fault-tolerant time-sensitive networking with IEEE 802.1CB
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2024 (English)In: IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems, ISSN 0278-0070, E-ISSN 1937-4151Article in journal (Refereed) Epub ahead of print
Abstract [en]

Time-Sensitive Networking (TSN) has become the most popular technique in modern safety-critical Automotive and Industrial Automation Networks by providing deterministic transmission policies. However, the data of TSN messages may be affected by transient faults. IEEE 802.1CB, a reliability standard in TSN, protects against such faults by providing disjoint redundant routes for each stream. However, the unique assumption may present a new challenge, i.e., an inadequate number of redundant routes that may negatively impact stream scheduling. This paper presents an offline fault-tolerant TSN scheduling approach that considers such impacts for real-time streams (such as Time-Trigger (TT) and Audio Video Bridging (AVB) streams). Specifically, we intend to calculate the minimum upper bound number of disjoint routes required for each stream to meet the reliability requirements, subsequently enhancing the network’s schedulability. We also propose a service degradation function for AVB streams when the network is under heavy load caused by redundant transmissions of TT streams. This function will maintain schedulability and reliability for AVB streams. Experiments with small-and large-scale synthetic networks show the efficiency.

Place, publisher, year, edition, pages
Institute of Electrical and Electronics Engineers (IEEE), 2024
Keywords
Circuit faults, Computer network reliability, Fault tolerance, Fault tolerant systems, Fault-Tolerant Scheduling, Job shop scheduling, Number of Redundant routes, Reliability, Service Degradation, Time-Sensitive Networking, Time-Triggered Streams, Transient analysis
National Category
Computer Engineering
Identifiers
urn:nbn:se:umu:diva-220141 (URN)10.1109/TCAD.2024.3352925 (DOI)2-s2.0-85182925116 (Scopus ID)
Funder
Swedish Research Council, 2023-04485
Available from: 2024-02-13 Created: 2024-02-13 Last updated: 2024-02-13
Jiang, Z., Dai, X., Burns, A., Audsley, N., Gu, Z. & Gray, I. (2023). A high-resilience imprecise computing architecture for mixed-criticality systems. IEEE Transactions on Computers, 72(1), 29-42
Open this publication in new window or tab >>A high-resilience imprecise computing architecture for mixed-criticality systems
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2023 (English)In: IEEE Transactions on Computers, ISSN 0018-9340, E-ISSN 1557-9956, Vol. 72, no 1, p. 29-42Article in journal (Refereed) Published
Abstract [en]

Conventional mixed-criticality systems (MCS)s are designed to terminate the execution of less critical tasks in exceptional situations so that the timing properties of more critical tasks can be preserved. Such a strategy can be controversial and has proven difficult to implement in practice, as it can lead to hazards and reduced functionality due to the absence of the discarded tasks. To mitigate this issue, the imprecise mixed-critically system model (IMCS) has been proposed. In such a model, instead of completely dropping less-critical tasks, these tasks are executed as much as possible through the use of decreased computation precision. Although IMCS could effectively improve the survivability of the less-critical tasks, it also introduces three key drawbacks - run-time computation errors, real-time performance degradation, and lack of flexibility. In this paper, we present a novel IMCS framework, which can (i) mitigate the computation errors caused by imprecise computation; (ii) achieve real-time performance near to that of a conventional MCS; (iii) enhance system-level throughput; and (iv) provide flexibility for run-time configuration. We describe the design details of HIART-MCS, and then present the corresponding theoretical analysis and optimisation method for its run-time configuration. Finally, HIART-MCS is evaluated against other MCS frameworks using a variety of experimental metrics.

Place, publisher, year, edition, pages
IEEE, 2023
Keywords
Clocks, Computational modeling, Hardware, Hardware/Software Co-design, Imprecise Computing, Real-Time Mixed-Criticality Systems, Registers, Schedulability Analysis, Software, Task analysis, Timing
National Category
Computer Sciences Computer Engineering
Identifiers
urn:nbn:se:umu:diva-199468 (URN)10.1109/TC.2022.3202721 (DOI)000899952600004 ()2-s2.0-85137584727 (Scopus ID)
Available from: 2022-09-26 Created: 2022-09-26 Last updated: 2023-09-05Bibliographically approved
Saremi, A., Ramkumar, B., Ghaffari, G. & Gu, Z. (2023). An acoustic echo canceller optimized for hands-free speech telecommunication in large vehicle cabins. EURASIP Journal on Audio, Speech, and Music Processing, 2023(1), Article ID 39.
Open this publication in new window or tab >>An acoustic echo canceller optimized for hands-free speech telecommunication in large vehicle cabins
2023 (English)In: EURASIP Journal on Audio, Speech, and Music Processing, ISSN 1687-4714, E-ISSN 1687-4722, Vol. 2023, no 1, article id 39Article in journal (Refereed) Published
Abstract [en]

Acoustic echo cancelation (AEC) is a system identification problem that has been addressed by various techniques and most commonly by normalized least mean square (NLMS) adaptive algorithms. However, performing a successful AEC in large commercial vehicles has proved complicated due to the size and challenging variations in the acoustic characteristics of their cabins. Here, we present a wideband fully linear time domain NLMS algorithm for AEC that is enhanced by a statistical double-talk detector (DTD) and a voice activity detector (VAD). The proposed solution was tested in four main Volvo truck models, with various cabin geometries, using standard Swedish hearing-in-noise (HINT) sentences in the presence and absence of engine noise. The results show that the proposed solution achieves a high echo return loss enhancement (ERLE) of at least 25 dB with a fast convergence time, fulfilling ITU G.168 requirements. The presented solution was particularly developed to provide a practical compromise between accuracy and computational cost to allow its real-time implementation on commercial digital signal processors (DSPs). A real-time implementation of the solution was coded in C on an ARM Cortex M-7 DSP. The algorithmic latency was measured at less than 26 ms for processing each 50-ms buffer indicating the computational feasibility of the proposed solution for real-time implementation on common DSPs and embedded systems with limited computational and memory resources. MATLAB source codes and related audio files are made available online for reference and further development.

Place, publisher, year, edition, pages
Springer, 2023
Keywords
Acoustic echo cancelation, Adaptive filters, Automotive speech processing, Automotive voice assistant, Hands-free telephony, Keyword spotting, NLMS, Speech signal enhancement
National Category
Signal Processing
Identifiers
urn:nbn:se:umu:diva-215398 (URN)10.1186/s13636-023-00305-7 (DOI)2-s2.0-85173557384 (Scopus ID)
Available from: 2023-10-27 Created: 2023-10-27 Last updated: 2023-10-27Bibliographically approved
Yin, L., Sun, J., Zhou, J., Gu, Z. & Li, K. (2023). ECFA: an efficient convergent firefly algorithm for solving task scheduling problems in cloud-edge computing. IEEE Transactions on Services Computing, 1-14
Open this publication in new window or tab >>ECFA: an efficient convergent firefly algorithm for solving task scheduling problems in cloud-edge computing
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2023 (English)In: IEEE Transactions on Services Computing, ISSN 1939-1374, E-ISSN 1939-1374, p. 1-14Article in journal (Refereed) Published
Abstract [en]

In cloud-edge computing paradigms, the integration of edge servers and task offloading mechanisms has posed new challenges to developing task scheduling strategies. This paper proposes an efficient convergent firefly algorithm (ECFA) for scheduling security-critical tasks onto edge servers and the cloud datacenter. The proposed ECFA uses a probability-based mapping operator to convert an individual firefly into a scheduling solution, in order to associate the firefly space with the solution space. Distinct from the standard FA, ECFA employs a low-complexity position update strategy to enhance computational efficiency in solution exploration. In addition, we provide a rigorous theoretical analysis to justify that ECFA owns the capability of converging to the global best individual in the firefly space. Furthermore, we introduce the concept of boundary traps for analyzing firefly movement trajectories, and investigate whether ECFA would fall into boundary traps during the evolutionary procedure under different parameter settings. We create various testing instances to evaluate the performance of ECFA in solving the cloud-edge scheduling problem, demonstrating its superiority over FA-based and other competing metaheuristics. Evaluation results also validate that the parameter range derived from the theoretical analysis can prevent our algorithm from falling into boundary traps.

Place, publisher, year, edition, pages
IEEE, 2023
Keywords
Cloud computing, cloud-edge computing, Convergence, convergence proof, firefly algorithm, Processor scheduling, Scheduling, Servers, Task analysis, task scheduling, Trajectory, trajectory analysis
National Category
Computer Sciences Computer Systems
Identifiers
urn:nbn:se:umu:diva-212325 (URN)10.1109/TSC.2023.3293048 (DOI)2-s2.0-85164678733 (Scopus ID)
Funder
The Kempe Foundations
Available from: 2023-07-25 Created: 2023-07-25 Last updated: 2023-10-13Bibliographically approved
Zhang, Y.-W., Zheng, H. & Gu, Z. (2023). EDF-based energy-efficient semi-clairvoyant scheduling with graceful degradation. IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems
Open this publication in new window or tab >>EDF-based energy-efficient semi-clairvoyant scheduling with graceful degradation
2023 (English)In: IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems, ISSN 0278-0070, E-ISSN 1937-4151Article in journal (Refereed) Epub ahead of print
Abstract [en]

Recent works introduce a semi-clairvoyant model, in which the system mode transition is revealed on the arrival of high criticality jobs. To solve the problem of inconsistency between the correctness criterion for mixed-criticality systems (MCS) with a semi-clairvoyant and the actual situation, we study the problem of schedulability and energy in MCS with the semi-clairvoyant model in this paper. First, we propose a new correctness criterion for MCS with semi-clairvoyant and graceful degradation and develop the schedulability test based on Demand Bound Function methods denoted as SCS-GD. Second, we propose an energy-efficient semi-clairvoyant scheduling algorithm based on SCS-GD denoted as EE-SCS-GD. Finally, we conduct an experimental evaluation of SCS-GD and EE-SCS-GD by synthetically generated task sets. The experimental results show that SCS-GD can improve the schedulability ratio by 5.98% compared to existing algorithms while EE-SCS-GD can save 56.17% energy compared to SCS-GD.

Place, publisher, year, edition, pages
IEEE, 2023
Keywords
Degradation, DVFS, Dynamic scheduling, Energy consumption, Energy efficiency, energy management, graceful degradation, mixed-criticality, Scheduling algorithms, semi-clairvoyant, Switches, Task analysis
National Category
Computer Engineering
Identifiers
urn:nbn:se:umu:diva-216110 (URN)10.1109/TCAD.2023.3321970 (DOI)2-s2.0-85174801429 (Scopus ID)
Funder
The Kempe Foundations
Available from: 2023-11-14 Created: 2023-11-14 Last updated: 2024-01-04
Wu, Y., Zhang, L., Gu, Z., Lu, H. & Wan, S. (2023). Edge-AI-driven framework with efficient mobile network design for facial expression recognition. ACM Transactions on Embedded Computing Systems, 22(3), Article ID 57.
Open this publication in new window or tab >>Edge-AI-driven framework with efficient mobile network design for facial expression recognition
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2023 (English)In: ACM Transactions on Embedded Computing Systems, ISSN 1539-9087, E-ISSN 1558-3465, Vol. 22, no 3, article id 57Article in journal (Refereed) Published
Abstract [en]

Facial Expression Recognition (FER) in the wild poses significant challenges due to realistic occlusions, illumination, scale, and head pose variations of the facial images. In this article, we propose an Edge-AI-driven framework for FER. On the algorithms aspect, we propose two attention modules, Arbitrary-oriented Spatial Pooling (ASP) and Scalable Frequency Pooling (SFP), for effective feature extraction to improve classification accuracy. On the systems aspect, we propose an edge-cloud joint inference architecture for FER to achieve low-latency inference, consisting of a lightweight backbone network running on the edge device, and two optional attention modules partially offloaded to the cloud. Performance evaluation demonstrates that our approach achieves a good balance between classification accuracy and inference latency.

Place, publisher, year, edition, pages
Association for Computing Machinery (ACM), 2023
Keywords
cloud offloading, Deep learning, edge computing, Facial Expression Recognition
National Category
Computer Systems Computer Sciences
Identifiers
urn:nbn:se:umu:diva-212242 (URN)10.1145/3587038 (DOI)2-s2.0-85164280960 (Scopus ID)
Funder
The Kempe Foundations
Available from: 2023-07-20 Created: 2023-07-20 Last updated: 2023-07-20Bibliographically approved
Luan, S., Gu, Z. & Wan, S. (2023). Efficient performance prediction of end-to-end autonomous driving under continuous distribution shifts based on anomaly detection. Journal of Signal Processing Systems
Open this publication in new window or tab >>Efficient performance prediction of end-to-end autonomous driving under continuous distribution shifts based on anomaly detection
2023 (English)In: Journal of Signal Processing Systems, ISSN 1939-8018, E-ISSN 1939-8115Article in journal (Refereed) Epub ahead of print
Abstract [en]

A Deep Neural Network (DNN)’s prediction may be unreliable outside of its training distribution despite high levels of accuracy obtained during model training. The DNN may experience different degrees of accuracy degradation for different levels of distribution shifts, hence it is important to predict its performance (accuracy) under distribution shifts. In this paper, we consider the end-to-end approach to autonomous driving of using a DNN to map from an input image to the control action such as the steering angle. For each input image with possible perturbations that cause distribution shifts, we design a Performance Prediction Module to compute its anomaly score, and use it to predict the DNN’s expected prediction error, i.e., its expected deviation from the ground truth (optimal) control action, which is not available after deployment. If the expected prediction error is too large, then the DNN’s prediction may no longer be trusted, and remedial actions should be taken to ensure safety. We consider different methods for computing the anomaly score for the input image, including using the reconstruction error of an Autoencoder, or applying an Anomaly Detection algorithm to a hidden layer of the DNN. We present performance evaluation of the different methods in terms of both prediction accuracy and execution time on different hardware platforms, in order to provide a useful reference for the designer to choose among the different methods.

Place, publisher, year, edition, pages
Springer, 2023
Keywords
Machine learning, Deep learning, Distribution shifts, Performance prediction, End-to-end autonomous driving
National Category
Computer Sciences
Identifiers
urn:nbn:se:umu:diva-214350 (URN)10.1007/s11265-023-01893-5 (DOI)2-s2.0-85177063760 (Scopus ID)
Funder
The Kempe Foundations
Note

Originally included in thesis in manuscript form. 

Available from: 2023-09-12 Created: 2023-09-12 Last updated: 2023-12-04
Zhang, Y.-W., Chen, R.-K. & Gu, Z. (2023). Energy-Aware Partitioned Scheduling of Imprecise Mixed-Criticality Systems. IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems, 1-1
Open this publication in new window or tab >>Energy-Aware Partitioned Scheduling of Imprecise Mixed-Criticality Systems
2023 (English)In: IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems, ISSN 0278-0070, E-ISSN 1937-4151, p. 1-1Article in journal (Refereed) Published
Abstract [en]

We consider partitioned scheduling of an Imprecise Mixed-Criticality (IMC) taskset on a uniform multiprocessor platform, with Earliest Deadline First-Virtual Deadline (EDF-VD) as the uniprocessor task scheduling algorithm, and address the optimization problem of finding a feasible task-to-processor assignment and low-criticality (LO) mode processor speed with the objective of minimizing the system’s average energy consumption in LO mode. We propose a task-to-processor assignment algorithm Criticality-Unaware Worst-Fit Decreasing (CU-WFD) algorithm, which allocates tasks with the Worst-Fit Decreasing (WFD) heuristic method based on utilization values at their respective criticality levels. We determine the energy-efficient speed for each processor based on EDF-VD scheduling, and present our algorithm Energy-Efficient Partitioned Scheduling for Imprecise Mixed-Criticality (EEPSIMC) with the CU-WFD heuristic algorithm to minimize system energy consumption. The experimental results show that our proposed algorithm has good performance in terms both schedulability ratio and normalized energy consumption compared to seven comparison baselines.

Place, publisher, year, edition, pages
Institute of Electrical and Electronics Engineers (IEEE), 2023
Keywords
EDF-VD, Energy consumption, Energy efficiency, energy-aware scheduling, Heuristic algorithms, Imprecise mixed-criticality, Job shop scheduling, multiprocessor, partitioned scheduling, Partitioning algorithms, Switches, Task analysis
National Category
Computer Engineering Computer Sciences
Identifiers
urn:nbn:se:umu:diva-213599 (URN)10.1109/TCAD.2023.3246926 (DOI)2-s2.0-85149061429 (Scopus ID)
Available from: 2023-08-29 Created: 2023-08-29 Last updated: 2023-10-13Bibliographically approved
Luan, S., Gu, Z., Xu, R., Zhao, Q. & Chen, G. (2023). LRP-based network pruning and policy distillation of robust and non-robust DRL agents for embedded systems. Concurrency and Computation, 35(19), Article ID e7351.
Open this publication in new window or tab >>LRP-based network pruning and policy distillation of robust and non-robust DRL agents for embedded systems
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2023 (English)In: Concurrency and Computation, ISSN 1532-0626, E-ISSN 1532-0634, Vol. 35, no 19, article id e7351Article in journal (Refereed) Published
Abstract [en]

Reinforcement learning (RL) is an effective approach to developing control policies by maximizing the agent's reward. Deep reinforcement learning uses deep neural networks (DNNs) for function approximation in RL, and has achieved tremendous success in recent years. Large DNNs often incur significant memory size and computational overheads, which may impede their deployment into resource-constrained embedded systems. For deployment of a trained RL agent on embedded systems, it is necessary to compress the policy network of the RL agent to improve its memory and computation efficiency. In this article, we perform model compression of the policy network of an RL agent by leveraging the relevance scores computed by layer-wise relevance propagation (LRP), a technique for Explainable AI (XAI), to rank and prune the convolutional filters in the policy network, combined with fine-tuning with policy distillation. Performance evaluation based on several Atari games indicates that our proposed approach is effective in reducing model size and inference time of RL agents. We also consider robust RL agents trained with RADIAL-RL versus standard RL agents, and show that a robust RL agent can achieve better performance (higher average reward) after pruning than a standard RL agent for different attack strengths and pruning rates.

Place, publisher, year, edition, pages
John Wiley & Sons, 2023
Keywords
embedded systems, knowledge distillation, policy distillation, reinforcement learning
National Category
Computer Sciences
Identifiers
urn:nbn:se:umu:diva-200565 (URN)10.1002/cpe.7351 (DOI)000868806400001 ()2-s2.0-85139981238 (Scopus ID)
Note

Special Issue. 

First published online October 2022.

Available from: 2022-12-01 Created: 2022-12-01 Last updated: 2023-11-09Bibliographically approved
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ORCID iD: ORCID iD iconorcid.org/0000-0003-4228-2774

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