{ "@context": "https://schema.org", "@graph": [ { "@type": "ScholarlyArticle", "@id": "https://roozbeh.nl/publications/p2w-power-traces-to-weights/", "url": "https://roozbeh.nl/publications/p2w-power-traces-to-weights/", "mainEntityOfPage": "https://roozbeh.nl/publications/p2w-power-traces-to-weights/", "headline": "P2W: From Power Traces to Weights Matrix - An Unconventional Transfer Learning Approach", "name": "P2W: From Power Traces to Weights Matrix - An Unconventional Transfer Learning Approach", "abstract": "The rapid growth of deploying machine learning (ML) models within embedded systems on a chip (SoCs) has led to transformative shifts in fields like healthcare and autonomous vehicles. One of the primary challenges for training such embedded ML models is the lack of publicly available high-quality training data. Transfer learning approaches address this challenge by utilizing the knowledge encapsulated in an existing ML model as a starting point for training a new ML model. However, existing transfer learning approaches require direct access to the existing model which is not always feasible, especially for ML models deployed on embedded SoCs. Therefore, in this paper, we introduce a novel unconventional transfer learning approach to train a new ML model by extracting and using weights from an existing ML model running on an embedded SoC without having access to the model within the SoC. Our approach captures power consumption measurements from the SoC while it is executing the ML model and translates them to an approximated weights matrix used to initialize the new ML model. This improves the learning efficiency and predictive performance of the new model, especially in scenarios with limited data available to train the model. Our novel approach can effectively increase the accuracy of the new ML model up to 3 times compared to classical training methods using the same amount of limited training data.", "description": "Turns power traces captured from an embedded SoC into an approximate weight matrix that initialises a new model when the original model and its training data are out of reach.", "author": [ { "@type": "Person", "@id": "https://roozbeh.nl/#person", "name": "Roozbeh Siyadatzadeh", "url": "https://roozbeh.nl/" }, { "@type": "Person", "name": "Fatemeh Mehrafrooz", "givenName": "Fatemeh", "familyName": "Mehrafrooz", "sameAs": "https://orcid.org/0000-0001-5844-3793" }, { "@type": "Person", "name": "Nele Mentens", "givenName": "Nele", "familyName": "Mentens", "sameAs": "https://orcid.org/0000-0001-8753-7895" }, { "@type": "Person", "name": "Todor Stefanov", "givenName": "Todor", "familyName": "Stefanov", "sameAs": "https://orcid.org/0000-0001-6006-9366" } ], "datePublished": "2026-03-23", "publisher": { "@type": "Organization", "name": "ACM" }, "isPartOf": { "@type": "Book", "name": "Proceedings of the 41st ACM/SIGAPP Symposium on Applied Computing (SAC 2026)" }, "keywords": "Transfer learning, Power side-channel analysis, Embedded machine learning, System-on-chip, Embedded machine learning, Power side channels, Transfer learning, Model extraction, System-on-chip", "inLanguage": "en", "identifier": [ { "@type": "PropertyValue", "propertyID": "DOI", "value": "10.1145/3748522.3779939" } ], "sameAs": [ "https://doi.org/10.1145/3748522.3779939", "https://arxiv.org/abs/2502.14968" ], "isAccessibleForFree": true, "creativeWorkStatus": "Published", "pageStart": "653", "pageEnd": "661", "locationCreated": { "@type": "Place", "name": "Thessaloniki, Greece" }, "license": "https://creativecommons.org/licenses/by/4.0/", "image": "https://roozbeh.nl/og/paper-p2w-power-traces-to-weights.png", "funding": [ { "@type": "Grant", "identifier": "101070634", "name": "Horizon Europe project NeuroSoC", "funder": { "@type": "Organization", "name": "European Union" } } ], "encoding": [ { "@type": "MediaObject", "name": "arXiv preprint, Feb 2025 (PDF)", "encodingFormat": "application/pdf", "contentUrl": "https://roozbeh.nl/papers/P2W.pdf", "contentSize": "895585" } ], "associatedMedia": [ { "@type": "AudioObject", "name": "Audio overview of P2W: From Power Traces to Weights Matrix - An Unconventional Transfer Learning Approach", "description": "AI-generated conversational overview produced with Google NotebookLM from the paper. 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A companion to the paper, not a substitute for it.", "contentUrl": "https://roozbeh.nl/videos/P2W.mp4", "thumbnailUrl": "https://roozbeh.nl/videos/P2W-poster.webp", "encodingFormat": "video/mp4", "duration": "PT6M54S", "inLanguage": "en" } ] }, { "@type": "ScholarlyArticle", "@id": "https://roozbeh.nl/publications/security-analysis-cnn-partitioning-edge/", "url": "https://roozbeh.nl/publications/security-analysis-cnn-partitioning-edge/", "mainEntityOfPage": "https://roozbeh.nl/publications/security-analysis-cnn-partitioning-edge/", "headline": "A Security Analysis of CNN Partitioning Strategies for Distributed Inference at the Edge", "name": "A Security Analysis of CNN Partitioning Strategies for Distributed Inference at the Edge", "abstract": "The inference of Convolutional Neural Networks (CNNs) at the Edge poses significant challenges due to resource limitations of edge devices. One approach to addressing these challenges is to distribute a CNN model across multiple edge devices. While much attention has been paid to improving the performance, memory utilization, energy efficiency, and robustness of distributed CNN inference at the Edge, security implications of such inference remain largely unexplored. Therefore, in this paper, we investigate the security vulnerabilities of the three main partitioning strategies for distributing CNN models across multiple edge devices, namely vertical partitioning, horizontal partitioning, and data partitioning. More specifically, we assess how accurately an attacker can reconstruct the input image given to a CNN model and predict the image class by eavesdropping on the communication link between two edge devices. We devise a simple, yet realistic attack scenario in which the attacker attempts to reconstruct the input image from intermediate data obtained from the communication link. In order to evaluate the vulnerability of the system, the reconstructed image is fed back into the model to see if its class can be determined. We conduct extensive experiments using different CNN models and datasets. Our results show that data partitioning is less vulnerable to this attack scenario compared to the other partitioning strategies, while vertical partitioning is the most vulnerable.", "description": "Compares vertical, horizontal, and data partitioning of CNNs across edge devices against an eavesdropper who reconstructs the input image from intercepted intermediate data.", "author": [ { "@type": "Person", "name": "Fatemeh Mehrafrooz", "givenName": "Fatemeh", "familyName": "Mehrafrooz", "sameAs": "https://orcid.org/0000-0001-5844-3793" }, { "@type": "Person", "@id": "https://roozbeh.nl/#person", "name": "Roozbeh Siyadatzadeh", "url": "https://roozbeh.nl/" }, { "@type": "Person", "name": "Nele Mentens", "givenName": "Nele", "familyName": "Mentens", "sameAs": "https://orcid.org/0000-0001-8753-7895" }, { "@type": "Person", "name": "Todor Stefanov", "givenName": "Todor", "familyName": "Stefanov", "sameAs": "https://orcid.org/0000-0001-6006-9366" } ], "datePublished": "2026-02-12", "publisher": { "@type": "Organization", "name": "Springer Nature Switzerland" }, "isPartOf": { "@type": "Book", "name": "Security, Privacy, and Applied Cryptography Engineering (SPACE 2025)", "isbn": [ "978-3-032-16341-7", "978-3-032-16342-4" ], "isPartOf": { "@type": "BookSeries", "name": "Lecture Notes in Computer Science", "issn": [ "0302-9743", "1611-3349" ] }, "volumeNumber": "16406" }, "keywords": "Distributed edge computing, Neural networks, Security, Distributed edge AI, CNN security, Model partitioning, Input reconstruction, Privacy", "inLanguage": "en", "identifier": [ { "@type": "PropertyValue", "propertyID": "DOI", "value": "10.1007/978-3-032-16342-4_15" } ], "sameAs": [ "https://doi.org/10.1007/978-3-032-16342-4_15" ], "isAccessibleForFree": true, "creativeWorkStatus": "Published", "pageStart": "262", "pageEnd": "281", "locationCreated": { "@type": "Place", "name": "Guwahati, India" }, "image": "https://roozbeh.nl/og/paper-security-analysis-cnn-partitioning-edge.png", "encoding": [ { "@type": "MediaObject", "name": "Author manuscript (PDF)", "encodingFormat": "application/pdf", "contentUrl": "https://roozbeh.nl/papers/SPACE_25.pdf", "contentSize": "787169" } ], "associatedMedia": [ { "@type": "AudioObject", "name": "Audio overview of A Security Analysis of CNN Partitioning Strategies for Distributed Inference at the Edge", "description": "AI-generated conversational overview produced with Google NotebookLM from the paper. 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This promising approach relies on analog-to-digital converters (ADCs) to enable the integration of AIMC tiles into larger digital systems. In this paper, we investigate the vulnerability of AIMC tiles to power side-channel attacks targeting these ADCs. Specifically, we demonstrate that the numerical values of weights stored in an AIMC tile, that are often a critical asset of an ML model, can be extracted by analyzing the power consumption of the ADCs. With this objective in mind, we propose TraceFormer, which is a two-phase method: 1) we train a Transformer neural network (NN) model to translate captured ADC power traces into digital output values; 2) we utilize a novel input-controlled weight isolation technique in order to isolate each individual weight within the AIMC tile, and then reveal the isolated weight’s value by ADC power side-channel analysis using the Transformer NN model. We demonstrate the practical applicability and robustness of our proposed method by power side-channel analysis of Oscillator-based ADCs that are typically integrated within AIMC tiles. The experimental results show high accuracy and robustness of our Transformer-based analysis, implying potential vulnerabilities in AIMC tiles.", "description": "Shows that the weights stored in an analog in-memory computing tile can be recovered from the power consumption of its ADCs, using a Transformer and an input-controlled weight-isolation technique.", "author": [ { "@type": "Person", "@id": "https://roozbeh.nl/#person", "name": "Roozbeh Siyadatzadeh", "url": "https://roozbeh.nl/" }, { "@type": "Person", "name": "Fatemeh Mehrafrooz", "givenName": "Fatemeh", "familyName": "Mehrafrooz", "sameAs": "https://orcid.org/0000-0001-5844-3793" }, { "@type": "Person", "name": "Nele Mentens", "givenName": "Nele", "familyName": "Mentens", "sameAs": "https://orcid.org/0000-0001-8753-7895" }, { "@type": "Person", "name": "Todor Stefanov", "givenName": "Todor", "familyName": "Stefanov", "sameAs": "https://orcid.org/0000-0001-6006-9366" } ], "datePublished": "2025-09-10", "publisher": { "@type": "Organization", "name": "IEEE" }, "isPartOf": { "@type": "Book", "name": "2025 28th Euromicro Conference on Digital System Design (DSD)" }, "keywords": "Analog in-memory computing, Vulnerabilities, Power side-channel analysis, ADC power analysis, Analog in-memory computing, Hardware security, Power side channels, Transformers, Weight extraction", "inLanguage": "en", "identifier": [ { "@type": "PropertyValue", "propertyID": "DOI", "value": "10.1109/DSD67783.2025.00092" } ], "sameAs": [ "https://doi.org/10.1109/DSD67783.2025.00092" ], "isAccessibleForFree": true, "creativeWorkStatus": "Published", "pageStart": "635", "pageEnd": "642", "locationCreated": { "@type": "Place", "name": "Salerno, Italy" }, "image": "https://roozbeh.nl/og/paper-traceformer-weight-extraction-aimc.png", "funding": [ { "@type": "Grant", "identifier": "101070634", "name": "Horizon Europe project NeuroSoC", "funder": { "@type": "Organization", "name": "European Union" } } ], "encoding": [ { "@type": "MediaObject", "name": "Author manuscript (PDF)", "encodingFormat": "application/pdf", "contentUrl": "https://roozbeh.nl/papers/TraceFormer.pdf", "contentSize": "983905" } ], "associatedMedia": [ { "@type": "AudioObject", "name": "Audio overview of TraceFormer: A Transformer-Based Method for Weight Extraction from AIMC Tiles", "description": "AI-generated conversational overview produced with Google NotebookLM from the paper. 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Due to the high probability of packet loss, energy limitation of IoT devices, and the external disturbances that may frequently occur on the fog infrastructure, the timing constraints of real-time tasks may be compromised. Therefore, the reliability of executing real-time tasks has always been a significant challenge in fog computing. In addition to the correct execution of the tasks, it is also important to execute them before their deadlines according to their real-time classification. State-of-the-art methods generally focus on the delay or functionality of tasks in fog computing systems. However, those methods do not widely focus on the reliability of tasks with real-time constraints in dynamic environments. In this paper, a novel primary backup task assignment strategy based on machine learning (ReLIEF) is proposed to improve the reliability of fog-based IoT systems. To identify suitable nodes for the execution of the primary and backup tasks, ReLIEF employs a Reinforcement Learning (RL) approach, which has an outstanding performance in dynamic environments by establishing a balance between communication delay and workload on each fog device. Based on the simulations, our newly proposed technique has been able to reduce the amount of task dropping rate by up to 84% against the state-of-the-art. Moreover, it is capable of balancing the workload distribution while increasing the reliability of the system by nearly 72% compared with its counterparts.", "description": "Reinforcement learning assigns primary and backup copies of real-time tasks to fog nodes, balancing communication delay against the workload on each device.", "author": [ { "@type": "Person", "@id": "https://roozbeh.nl/#person", "name": "Roozbeh Siyadatzadeh", "url": "https://roozbeh.nl/" }, { "@type": "Person", "name": "Fatemeh Mehrafrooz", "givenName": "Fatemeh", "familyName": "Mehrafrooz", "sameAs": "https://orcid.org/0000-0001-5844-3793" }, { "@type": "Person", "name": "Mohsen Ansari", "givenName": "Mohsen", "familyName": "Ansari", "sameAs": "https://orcid.org/0000-0002-4670-8608" }, { "@type": "Person", "name": "Bardia Safaei", "givenName": "Bardia", "familyName": "Safaei", "sameAs": "https://orcid.org/0000-0001-9504-8637" }, { "@type": "Person", "name": "Muhammad Shafique", "givenName": "Muhammad", "familyName": "Shafique", "sameAs": "https://orcid.org/0000-0002-2607-8135" }, { "@type": "Person", "name": "Jörg Henkel", "givenName": "Jörg", "familyName": "Henkel", "sameAs": "https://orcid.org/0000-0001-9602-2922" }, { "@type": "Person", "name": "Alireza Ejlali", "givenName": "Alireza", "familyName": "Ejlali", "sameAs": "https://orcid.org/0000-0002-5661-3629" } ], "datePublished": "2023-06-15", "publisher": { "@type": "Organization", "name": "IEEE" }, "isPartOf": { "@type": "PublicationIssue", "issueNumber": "12", "isPartOf": { "@type": "PublicationVolume", "volumeNumber": "10", "isPartOf": { "@type": "Periodical", "name": "IEEE Internet of Things Journal", "issn": [ "2327-4662", "2372-2541" ] } } }, "keywords": "Internet of Things, Fog computing, Reliability, Reinforcement learning, Resource allocation, Reinforcement learning, Fog computing, Real-time systems, Fault tolerance, Resource allocation", "inLanguage": "en", "identifier": [ { "@type": "PropertyValue", "propertyID": "DOI", "value": "10.1109/JIOT.2023.3240007" } ], "sameAs": [ "https://doi.org/10.1109/JIOT.2023.3240007" ], "isAccessibleForFree": true, "creativeWorkStatus": "Published", "pageStart": "10752", "pageEnd": "10763", "image": "https://roozbeh.nl/og/paper-relief-rl-task-assignment-fog.png", "encoding": [ { "@type": "MediaObject", "name": "Author manuscript (PDF)", "encodingFormat": "application/pdf", "contentUrl": "https://roozbeh.nl/papers/ReLIEF.pdf", "contentSize": "1949091" } ], "associatedMedia": [ { "@type": "AudioObject", "name": "Audio overview of ReLIEF: A Reinforcement-Learning-Based Real-Time Task Assignment Strategy in Emerging Fault-Tolerant Fog Computing", "description": "AI-generated conversational overview produced with Google NotebookLM from the paper. A companion to the paper, not a substitute for it.", "contentUrl": "https://roozbeh.nl/audio/ReLIEF.m4a", "encodingFormat": "audio/mp4", "duration": "PT5M9S", "inLanguage": "en" }, { "@type": "VideoObject", "name": "Video overview of ReLIEF: A Reinforcement-Learning-Based Real-Time Task Assignment Strategy in Emerging Fault-Tolerant Fog Computing", "description": "AI-generated video overview produced with Google NotebookLM from the paper. A companion to the paper, not a substitute for it.", "contentUrl": "https://roozbeh.nl/videos/ReLIEF.mp4", "thumbnailUrl": "https://roozbeh.nl/videos/ReLIEF-poster.webp", "encodingFormat": "video/mp4", "duration": "PT7M28S", "inLanguage": "en" } ] }, { "@type": "ScholarlyArticle", "@id": "https://roozbeh.nl/publications/atlas-aging-aware-task-replication/", "url": "https://roozbeh.nl/publications/atlas-aging-aware-task-replication/", "mainEntityOfPage": "https://roozbeh.nl/publications/atlas-aging-aware-task-replication/", "headline": "ATLAS: Aging-Aware Task Replication for Multicore Safety-Critical Systems", "name": "ATLAS: Aging-Aware Task Replication for Multicore Safety-Critical Systems", "abstract": "A major requirement of safety-critical systems is high reliability at low power consumption. Dynamic voltage and frequency (v/f) scaling (DVFS) techniques are widely exploited to reduce power consumption. However, DVFS through downscaling v/f levels has a negative impact on the reliability of the tasks running on the cores, and through upscaling v/f levels has circuit-level aging effects. To achieve high reliability in multicore safety-critical systems, task replication as a fault-tolerant technique is an established way to deal with the negative effect of downscaling v/f levels, but it may accelerate aging effects due to elevating the on-chip temperatures. In this paper, we propose an aging-aware task replication (called ATLAS) method that solves the problem of satisfying the desired reliability target for a set of periodic hard real-time tasks which are executed on a multicore system. The proposed method satisfies the reliability target of the tasks through updating the required number of replicas for each task at different years. We replicate the tasks through our proposed formulas such that the reliability target is satisfied. However, task replication increases the temperature of the system and accelerates aging. To decelerate aging, we attempt to reduce the temperature while mapping and scheduling the tasks. We have also developed a modified demand bound function (DBF) for our aging-aware task replication method to verify scheduling the real-time tasks. Compared to the existing state-of-the-art techniques, experimental results for safety-critical applications on different configurations of multicore systems demonstrate the efficiency and effectiveness of our proposed method. Experiments show that our proposed method improves schedulability on average by 16.1% and reduces the temperature on average by 7.4°C compared to state-of-the-art methods while meeting the system reliability target.", "description": "Aging-aware task replication that keeps periodic hard real-time tasks at their reliability target over a multicore system’s lifetime while limiting the temperature that accelerates aging.", "author": [ { "@type": "Person", "name": "Mohsen Ansari", "givenName": "Mohsen", "familyName": "Ansari", "sameAs": "https://orcid.org/0000-0002-4670-8608" }, { "@type": "Person", "name": "Sepideh Safari", "givenName": "Sepideh", "familyName": "Safari" }, { "@type": "Person", "name": "Amir Yeganeh-Khaksar", "givenName": "Amir", "familyName": "Yeganeh-Khaksar" }, { "@type": "Person", "@id": "https://roozbeh.nl/#person", "name": "Roozbeh Siyadatzadeh", "url": "https://roozbeh.nl/" }, { "@type": "Person", "name": "Pourya Gohari-Nazari", "givenName": "Pourya", "familyName": "Gohari-Nazari" }, { "@type": "Person", "name": "Heba Khdr", "givenName": "Heba", "familyName": "Khdr" }, { "@type": "Person", "name": "Muhammad Shafique", "givenName": "Muhammad", "familyName": "Shafique", "sameAs": "https://orcid.org/0000-0002-2607-8135" }, { "@type": "Person", "name": "Jörg Henkel", "givenName": "Jörg", "familyName": "Henkel", "sameAs": "https://orcid.org/0000-0001-9602-2922" }, { "@type": "Person", "name": "Alireza Ejlali", "givenName": "Alireza", "familyName": "Ejlali", "sameAs": "https://orcid.org/0000-0002-5661-3629" } ], "datePublished": "2023-05-09", "publisher": { "@type": "Organization", "name": "IEEE" }, "isPartOf": { "@type": "Book", "name": "2023 IEEE 29th Real-Time and Embedded Technology and Applications Symposium (RTAS)" }, "keywords": "Task replication, Aging, Reliability, Real-time systems, Multicore, Real-time systems, Safety-critical systems, Task replication, Processor aging, Thermal management", "inLanguage": "en", "identifier": [ { "@type": "PropertyValue", "propertyID": "DOI", "value": "10.1109/RTAS58335.2023.00025" } ], "sameAs": [ "https://doi.org/10.1109/RTAS58335.2023.00025" ], "isAccessibleForFree": false, "creativeWorkStatus": "Published", "pageStart": "223", "pageEnd": "234", "locationCreated": { "@type": "Place", "name": "San Antonio, TX, USA" }, "image": "https://roozbeh.nl/og/paper-atlas-aging-aware-task-replication.png", "associatedMedia": [ { "@type": "AudioObject", "name": "Audio overview of ATLAS: Aging-Aware Task Replication for Multicore Safety-Critical Systems", "description": "AI-generated conversational overview produced with Google NotebookLM from the paper. A companion to the paper, not a substitute for it.", "contentUrl": "https://roozbeh.nl/audio/ATLAS.m4a", "encodingFormat": "audio/mp4", "duration": "PT15M53S", "inLanguage": "en" } ] }, { "@type": "ScholarlyArticle", "@id": "https://roozbeh.nl/publications/passive-primary-backup-scheduling-heterogeneous/", "url": "https://roozbeh.nl/publications/passive-primary-backup-scheduling-heterogeneous/", "mainEntityOfPage": "https://roozbeh.nl/publications/passive-primary-backup-scheduling-heterogeneous/", "headline": "Passive Primary/Backup-Based Scheduling for Simultaneous Power and Reliability Management on Heterogeneous Embedded Systems", "name": "Passive Primary/Backup-Based Scheduling for Simultaneous Power and Reliability Management on Heterogeneous Embedded Systems", "abstract": "In addition to meeting the real-time constraint, power/energy efficiency and high reliability are two vital objectives for real-time embedded systems. Recently, heterogeneous multicore systems have been considered an appropriate solution for achieving joint power/energy efficiency and high reliability. However, power/energy and reliability are two conflict requirements due to the inherent redundancy of fault-tolerance techniques. Also, because of the heterogeneity of the system, the execution of the tasks, especially real-time tasks, in the heterogeneous system is more complicated than the homogeneous system. The proposed method in this paper employs a passive primary/backup technique to preserve the reliability requirement of the system at a satisfactory level and reduces power/energy consumption in heterogeneous multicore systems by considering real-time and peak power constraints. The proposed method attempts to map the primary and backup tasks in a mixed manner to benefit from the execution of the tasks in different core types and schedules the backup tasks after finishing the primary tasks to remove the overlap between the execution of the primary and backup tasks. Compared to the existing state-of-the-art methods, experimental results demonstrate our proposed method’s power efficiency and effectiveness in terms of schedulability.", "description": "Passive primary/backup scheduling on heterogeneous multicores that mixes task mapping across core types and runs backups after primaries to remove execution overlap.", "author": [ { "@type": "Person", "name": "Sina Yari-Karin", "givenName": "Sina", "familyName": "Yari-Karin", "sameAs": "https://orcid.org/0000-0003-0038-8642" }, { "@type": "Person", "@id": "https://roozbeh.nl/#person", "name": "Roozbeh Siyadatzadeh", "url": "https://roozbeh.nl/" }, { "@type": "Person", "name": "Mohsen Ansari", "givenName": "Mohsen", "familyName": "Ansari", "sameAs": "https://orcid.org/0000-0002-4670-8608" }, { "@type": "Person", "name": "Alireza Ejlali", "givenName": "Alireza", "familyName": "Ejlali", "sameAs": "https://orcid.org/0000-0002-5661-3629" } ], "datePublished": "2023-01-01", "publisher": { "@type": "Organization", "name": "IEEE" }, "isPartOf": { "@type": "PublicationIssue", "issueNumber": "1", "isPartOf": { "@type": "PublicationVolume", "volumeNumber": "8", "isPartOf": { "@type": "Periodical", "name": "IEEE Transactions on Sustainable Computing", "issn": [ "2377-3782", "2377-3790" ] } } }, "keywords": "Embedded systems, Fault tolerance, Real-time scheduling, Heterogeneous multicore systems, Power/energy consumption, Thermal design power, Heterogeneous multicore systems, Real-time scheduling, Fault tolerance, Energy efficiency, Reliability", "inLanguage": "en", "identifier": [ { "@type": "PropertyValue", "propertyID": "DOI", "value": "10.1109/TSUSC.2022.3186656" } ], "sameAs": [ "https://doi.org/10.1109/TSUSC.2022.3186656" ], "isAccessibleForFree": true, "creativeWorkStatus": "Published", "pageStart": "82", "pageEnd": "93", "image": "https://roozbeh.nl/og/paper-passive-primary-backup-scheduling-heterogeneous.png", "encoding": [ { "@type": "MediaObject", "name": "Author manuscript (PDF)", "encodingFormat": "application/pdf", "contentUrl": "https://roozbeh.nl/papers/tsusc.pdf", "contentSize": "892868" } ], "associatedMedia": [ { "@type": "AudioObject", "name": "Audio overview of Passive Primary/Backup-Based Scheduling for Simultaneous Power and Reliability Management on Heterogeneous Embedded Systems", "description": "AI-generated conversational overview produced with Google NotebookLM from the paper. A companion to the paper, not a substitute for it.", "contentUrl": "https://roozbeh.nl/audio/tsusc.m4a", "encodingFormat": "audio/mp4", "duration": "PT4M44S", "inLanguage": "en" } ] } ] }