Publications

@article{10.1145/3762640,

title = {Runtime Adaptivity for Efficient Neural Network Inference on Autonomous Systems},

author = {Danny Abraham and Biswadip Maity and Bryan Donyanavard and Nikil Dutt},

url = {https://doi.org/10.1145/3762640},

doi = {10.1145/3762640},

issn = {1539-9087},

year  = {2025},

date = {2025-10-01},

urldate = {2025-10-01},

journal = {ACM Trans. Embed. Comput. Syst.},

volume = {24},

number = {6},

publisher = {Association for Computing Machinery},

address = {New York, NY, USA},

abstract = {Neural network pruning and dynamic training have emerged as key techniques for optimizing deep learning models to meet the constraints of resource-limited systems. However, achieving both efficiency and adaptability without compromising safety or performance remains a significant challenge in real-time autonomous applications. We present Back to the Future and USA-Nets, two complementary approaches that address this challenge. Back to the Future combines pruning with dynamic routing to enable latency gains and dynamic reconfiguration at runtime, allowing a pruned model to seamlessly revert to the full model when unsafe or anomalous behavior is detected. USA-Nets extend this concept by enabling runtime adaptability through dynamically trained networks that can adjust their width without requiring additional annotated data or excessive storage overhead. Together, these methods deliver significant performance improvements while maintaining safety and flexibility, as evidenced by experimental results demonstrating that Back to the Future achieves a 32× faster reversion time compared to loading the full model, and USA-Nets achieve up to 85% latency reduction with minimal accuracy degradation. These innovations pave the way for efficient, adaptable, and safe deployment of deep learning models in diverse real-time and resource-constrained environments, with future work focusing on advanced pruning techniques and runtime optimizations.},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{10.1145/3771770,

title = {ACCESS-AV: Adaptive Communication-Computation Codesign for Sustainable Autonomous Vehicle Localization in Smart Factories},

author = {Rajat Bhattacharjya and Arnab Sarkar and Ish Kool and Sabur Baidya and Nikil Dutt},

url = {https://doi.org/10.1145/3771770},

doi = {10.1145/3771770},

issn = {1539-9087},

year  = {2025},

date = {2025-10-01},

urldate = {2025-10-01},

journal = {ACM Trans. Embed. Comput. Syst.},

publisher = {Association for Computing Machinery},

address = {New York, NY, USA},

abstract = {Autonomous Delivery Vehicles (ADVs) are increasingly used for transporting goods in 5G network-enabled smart factories, with the compute-intensive localization module presenting a significant opportunity for optimization. We propose ACCESS-AV, an energy-efficient Vehicle-to-Infrastructure (V2I) localization framework that leverages existing 5G infrastructure in smart factory environments. By opportunistically accessing the periodically broadcast 5G Synchronization Signal Blocks (SSBs) for localization, ACCESS-AV obviates the need for dedicated Roadside Units (RSUs) or additional onboard sensors to achieve energy efficiency as well as cost reduction. We implement an Angle-of-Arrival (AoA)-based estimation method using the Multiple Signal Classification (MUSIC) algorithm, optimized for resource-constrained ADV platforms through an adaptive communication-computation strategy that dynamically balances energy consumption with localization accuracy based on environmental conditions such as Signal-to-Noise Ratio (SNR) and vehicle velocity. Experimental results demonstrate that ACCESS-AV achieves an average energy reduction of 43.09% compared to non-adaptive systems employing AoA algorithms such as vanilla MUSIC, ESPRIT, and Root-MUSIC. It maintains sub-30 cm localization accuracy while also delivering substantial reductions in infrastructure and operational costs, establishing its viability for sustainable smart factory environments.},

note = {Just Accepted},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{10.1145/3761808,

title = {OASIS: Optimized Adaptive System for Intelligent SLAM},

author = {Alles Rebel and Nikil Dutt and Bryan Donyanavard},

url = {https://doi.org/10.1145/3761808},

doi = {10.1145/3761808},

issn = {1539-9087},

year  = {2025},

date = {2025-09-01},

urldate = {2025-09-01},

journal = {ACM Trans. Embed. Comput. Syst.},

volume = {24},

number = {5s},

publisher = {Association for Computing Machinery},

address = {New York, NY, USA},

abstract = {Visual Simultaneous Localization and Mapping (VSLAM) is essential for mobile autonomous systems operating in complex dynamic environments. VSLAM algorithms are computationally intensive and must execute in real-time on resource-constrained embedded devices. Variations in environmental complexity can lead to longer frame processing times, causing dropped frames, lost localization information, and degraded accuracy. To address these challenges, we introduce OASIS, a novel adaptive approximation method that dynamically reduces input frame areas based on real-time visual importance. Unlike traditional optimizations that require adjusting internal SLAM parameters, OASIS selectively minimizes computation by adaptively filtering less critical image regions, significantly reducing computational load. Evaluations on the EuRoC MAV dataset demonstrate that our approach balances accuracy and system predictability, achieving up to a 71.8% reduction in worst-case pose estimation errors. OASIS offers a significant advancement in reliable, predictable, and energy-efficient SLAM tailored for mobile autonomous robotic applications.},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{10.1145/3723357,

title = {Exploiting Approximation for Run-time Resource Management of Embedded HMPs},

author = {Zain Taufique and Anil Kanduri and Antonio Miele and Amir Rahmani and Cristiana Bolchini and Nikil Dutt and Pasi Liljeberg},

url = {https://doi.org/10.1145/3723357},

doi = {10.1145/3723357},

issn = {1539-9087},

year  = {2025},

date = {2025-04-01},

journal = {ACM Trans. Embed. Comput. Syst.},

volume = {24},

number = {3},

publisher = {Association for Computing Machinery},

address = {New York, NY, USA},

abstract = {Run-time resource management (RTM) of multi-programmed workloads on heterogeneous multi-core platforms is challenging due to (i) fixed power budget of the device, (ii) variable performance requirements of the workloads, and (iii) unknown arrival of the applications. Existing RTM solutions lack power-performance coordination, resulting in performance degradation during power actuation or power violations during performance provisioning. Exploiting inherent error-resilience of the applications can address the performance loss incurred in power actuation, by combining run-time approximation with traditional power knobs (including Dynamic Voltage/Frequency Scaling, Task Migration, Degree of Parallelism, and CPU Quota). In this work, we present an accuracy-aware resource management framework that jointly actuates run-time approximation and traditional power knobs for efficient power-performance management of multi-programmed and multi-threaded workloads running on heterogeneous mobile platforms. Our strategy configures the accuracy of the applications at run-time to exploit accuracy-performance trade-offs, by considering system-wide power-performance dynamics. We use heuristic estimation models to jointly enforce accuracy configuration and traditional power knobs settings at run-time. We evaluated our framework on real-world embedded mobile platforms, including Odroid XU3 and Asus Tinker Edge R boards to demonstrate the efficiency of our proposed approach across multiple workload scenarios. Our approach achieved 25% lower performance violations against the state-of-the-art run-time resource management policies at the cost of 2.2% accuracy loss across six applications.},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{info:doi/10.2196/67969,

title = {Multimodal Pain Recognition in Postoperative Patients: Machine Learning Approach},

author = {Ajan Subramanian and Rui Cao and Emad Kasaeyan Naeini and Seyed Amir Hossein Aqajari and Thomas D Hughes and Michael-David Calderon and Kai Zheng and Nikil Dutt and Pasi Liljeberg and Sanna Salanterä and Ariana M Nelson and Amir M Rahmani},

url = {https://doi.org/10.2196/67969},

doi = {10.2196/67969},

issn = {2561-326X},

year  = {2025},

date = {2025-01-27},

journal = {JMIR Form Res},

volume = {9},

pages = {e67969},

abstract = {Background: Acute pain management is critical in postoperative care, especially in vulnerable patient populations that may be unable to self-report pain levels effectively. Current methods of pain assessment often rely on subjective patient reports or behavioral pain observation tools, which can lead to inconsistencies in pain management. Multimodal pain assessment, integrating physiological and behavioral data, presents an opportunity to create more objective and accurate pain measurement systems. However, most previous work has focused on healthy subjects in controlled environments, with limited attention to real-world postoperative pain scenarios. This gap necessitates the development of robust, multimodal approaches capable of addressing the unique challenges associated with assessing pain in clinical settings, where factors like motion artifacts, imbalanced label distribution, and sparse data further complicate pain monitoring. Objective: This study aimed to develop and evaluate a multimodal machine learning–based framework for the objective assessment of pain in postoperative patients in real clinical settings using biosignals such as electrocardiogram, electromyogram, electrodermal activity, and respiration rate (RR) signals. Methods: The iHurt study was conducted on 25 postoperative patients at the University of California, Irvine Medical Center. The study captured multimodal biosignals during light physical activities, with concurrent self-reported pain levels using the Numerical Rating Scale. Data preprocessing involved noise filtering, feature extraction, and combining handcrafted and automatic features through convolutional and long-short-term memory autoencoders. Machine learning classifiers, including support vector machine, random forest, adaptive boosting, and k-nearest neighbors, were trained using weak supervision and minority oversampling to handle sparse and imbalanced pain labels. Pain levels were categorized into baseline and 3 levels of pain intensity (1-3). Results: The multimodal pain recognition models achieved an average balanced accuracy of over 80% across the different pain levels. RR models consistently outperformed other single modalities, particularly for lower pain intensities, while facial muscle activity (electromyogram) was most effective for distinguishing higher pain intensities. Although single-modality models, especially RR, generally provided higher performance compared to multimodal approaches, our multimodal framework still delivered results that surpassed most previous works in terms of overall accuracy. Conclusions: This study presents a novel, multimodal machine learning framework for objective pain recognition in postoperative patients. The results highlight the potential of integrating multiple biosignal modalities for more accurate pain assessment, with particular value in real-world clinical settings.},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{Niedermeier_2025,

title = {An integrated toolbox for creating neuromorphic edge applications},

author = {Lars Niedermeier and Nikil Dutt and Jeffrey L Krichmar},

url = {https://doi.org/10.1088/2634-4386/adad0f},

doi = {10.1088/2634-4386/adad0f},

year  = {2025},

date = {2025-01-01},

journal = {Neuromorphic Computing and Engineering},

volume = {5},

number = {1},

pages = {014003},

publisher = {IOP Publishing},

abstract = {spiking neural networks (SNNs) and neuromorphic models are believed to be more efficient in general and have more biological realism than the activation functions typically used in deep neural networks, transformer models and generative AI. SNNs have local learning rules, are able to learn on small data sets, and can adapt through neuromodulation. Although research has shown their advantages, there are still few compelling practical applications, especially at the edge where sensors and actuators need to be processed in a timely fashion. One reason for this might be that SNNs are much more challenging to understand, build, and operate due to their intrinsic properties. For instance, the mathematical foundation involves differential equations rather than basic activation functions. To address these challenges, we have developed CARLsim++, an integrated toolbox that facilitates the creation of neuromorphic applications. It extends the highly efficient CARLsim open-source SNN simulator. CARLsim++ encapsulates the mathematical intrinsics and low-level C++ programming by providing a graphical user interface for users to easily create their SNNs and a means to configure sensors and actuators for robotics and other edge devices. These can be accurately simulated before deploying on physical devices. CARLsim++ can lead to rapid development of neuromorphic applications for simulation or edge processing. We introduce CARLsim++ with a closed loop robotic demonstration using neuromorphic computing.},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{10.1145/3696425b,

title = {PERFECT: Personalized Exercise Recommendation Framework and architECTure},

author = {Milad Asgari Mehrabadi and Elahe Khatibi and Tamara Jimah and Sina Labbaf and Holly Borg and Laura Narvaez and Pamela Pimentel and Arlene Turner and Nikil Dutt and Yuqing Guo and Amir M. Rahmani},

url = {https://doi.org/10.1145/3696425},

doi = {10.1145/3696425},

year  = {2024},

date = {2024-11-01},

journal = {ACM Trans. Comput. Healthcare},

volume = {5},

number = {4},

publisher = {Association for Computing Machinery},

address = {New York, NY, USA},

abstract = {Background: The health benefits of regular physical activity (PA) are well-established and widely acknowledged. Through the integration of wearable trackers, the Internet of Things (IoT)—a network of interconnected devices capable of collecting and exchanging data—coupled with mobile health (mHealth), which refers to the use of mobile devices to support medical and public health practices, it is now feasible to systematically gather and present individual exercise behaviors. This advanced approach enables the precise correlation of users’ physiological data and daily activities with their specific fitness needs, offering a personalized pathway to improving health outcomes.Objective: This study aims to enhance PA levels among individuals by developing a personalized exercise recommendation system. Utilizing reinforcement learning, the system proposes tailored exercise plans based on biomarkers and the user’s specific context.Methods: In this study, we developed applications for smartphones and smartwatches designed to gather, monitor, and recommend exercise routines through the application of a contextual multi-arm bandit algorithm. To evaluate the efficacy of this mHealth exercise regimen, we enlisted the participation of twenty female college students.Results: The outcomes of our investigation revealed a significant enhancement in the average daily duration of exercise (P (lt) . 001). Participants expressed high levels of satisfaction with both the walking program and the recommendation system, achieving average ratings of 4.31 (SD (=) 0.60) and 3.69 (SD (=) 0.95), respectively, on a 5-point scale. Furthermore, the average scores for participants’ confidence in safely performing the recommended walking exercises, as well as their perception of the study’s effectiveness in meeting their PA needs, were both above 4, indicating a positive reception and confidence in the program’s design and implementation.Conclusions: The evolution of the IoT and wearable technology has marked the beginning of a new era for mHealth systems, particularly in the personalization of health interventions. Such advancements enable the precise personalization of PA recommendations, potentially enhancing user engagement and performance outcomes. This paper introduces a novel exercise recommendation system that utilizes reinforcement learning to personalize walking exercises based on the user’s biomarkers and context, aiming to improve the user’s aerobic capacity significantly.},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{10.1145/3696425,

title = {PERFECT: Personalized Exercise Recommendation Framework and architECTure},

author = {Milad Asgari Mehrabadi and Elahe Khatibi and Tamara Jimah and Sina Labbaf and Holly Borg and Laura Narvaez and Pamela Pimentel and Arlene Turner and Nikil Dutt and Yuqing Guo and Amir M. Rahmani},

url = {https://doi.org/10.1145/3696425},

doi = {10.1145/3696425},

year  = {2024},

date = {2024-11-01},

urldate = {2024-11-01},

journal = {ACM Trans. Comput. Healthcare},

volume = {5},

number = {4},

publisher = {Association for Computing Machinery},

address = {New York, NY, USA},

abstract = {Background: The health benefits of regular physical activity (PA) are well-established and widely acknowledged. Through the integration of wearable trackers, the Internet of Things (IoT)—a network of interconnected devices capable of collecting and exchanging data—coupled with mobile health (mHealth), which refers to the use of mobile devices to support medical and public health practices, it is now feasible to systematically gather and present individual exercise behaviors. This advanced approach enables the precise correlation of users’ physiological data and daily activities with their specific fitness needs, offering a personalized pathway to improving health outcomes.Objective: This study aims to enhance PA levels among individuals by developing a personalized exercise recommendation system. Utilizing reinforcement learning, the system proposes tailored exercise plans based on biomarkers and the user’s specific context.Methods: In this study, we developed applications for smartphones and smartwatches designed to gather, monitor, and recommend exercise routines through the application of a contextual multi-arm bandit algorithm. To evaluate the efficacy of this mHealth exercise regimen, we enlisted the participation of twenty female college students.Results: The outcomes of our investigation revealed a significant enhancement in the average daily duration of exercise (P (lt) . 001). Participants expressed high levels of satisfaction with both the walking program and the recommendation system, achieving average ratings of 4.31 (SD (=) 0.60) and 3.69 (SD (=) 0.95), respectively, on a 5-point scale. Furthermore, the average scores for participants’ confidence in safely performing the recommended walking exercises, as well as their perception of the study’s effectiveness in meeting their PA needs, were both above 4, indicating a positive reception and confidence in the program’s design and implementation.Conclusions: The evolution of the IoT and wearable technology has marked the beginning of a new era for mHealth systems, particularly in the personalization of health interventions. Such advancements enable the precise personalization of PA recommendations, potentially enhancing user engagement and performance outcomes. This paper introduces a novel exercise recommendation system that utilizes reinforcement learning to personalize walking exercises based on the user’s biomarkers and context, aiming to improve the user’s aerobic capacity significantly.},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{10.1145/3656172,

title = {ZoneTrace: A Zone Monitoring Tool for F2FS on ZNS SSDs},

author = {Ping-Xiang Chen and Dongjoo Seo and Changhoon Sung and Jongheum Park and Minchul Lee and Huaicheng Li and Matias Bjørling and Nikil Dutt},

url = {https://doi.org/10.1145/3656172},

doi = {10.1145/3656172},

issn = {1084-4309},

year  = {2024},

date = {2024-04-01},

urldate = {2024-04-01},

journal = {ACM Trans. Des. Autom. Electron. Syst.},

publisher = {Association for Computing Machinery},

address = {New York, NY, USA},

abstract = {We present ZoneTrace, a runtime monitoring tool for the Flash-Friendly File System (F2FS) on Zoned Namespace (ZNS) SSDs. ZNS SSD organizes its storage into zones of sequential write access. Due to ZNS SSD’s sequential write nature, F2FS is a log-structured file system that has recently been adopted to support ZNS SSDs. To present the space management with the zone concept between F2FS and the underlying ZNS SSD, we developed ZoneTrace, a tool that enables users to visualize and analyze the space management of F2FS on ZNS SSDs. ZoneTrace utilizes the extended Berkeley Packet Filter (eBPF) to trace the updated segment bitmap in F2FS and visualize each zone space usage accordingly. Furthermore, ZoneTrace is able to analyze on file fragmentation in F2FS and provides users with informative fragmentation histogram to serve as an indicator of file fragmentation. Using ZoneTrace’s visualization, we are able to identify the current F2FS space management scheme’s inability to fully optimize space for streaming data recording in autonomous systems, which leads to serious file fragmentation on ZNS SSDs. Our evaluations show that ZoneTrace is lightweight and assists users in getting useful insights for effortless monitoring on F2FS with ZNS SSD with both synthetic and realistic workloads. We believe ZoneTrace can help users analyze F2FS with ease and open up space management research topics with F2FS on ZNS SSDs.},

note = {Just Accepted},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{10779575,

title = {FDPFS: Leveraging File System Abstraction for FDP SSD Data Placement},

author = {Ping-Xiang Chen and Dongjoo Seo and Nikil Dutt},

doi = {10.1109/LES.2024.3443205},

year  = {2024},

date = {2024-01-01},

urldate = {2024-01-01},

journal = {IEEE Embedded Systems Letters},

volume = {16},

number = {4},

pages = {349-352},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{10779970,

title = {MUSIC-Lite: Efficient MUSIC Using Approximate Computing: An OFDM Radar Case Study},

author = {Rajat Bhattacharjya and Arnab Sarkar and Biswadip Maity and Nikil Dutt},

doi = {10.1109/LES.2024.3440208},

year  = {2024},

date = {2024-01-01},

urldate = {2024-01-01},

journal = {IEEE Embedded Systems Letters},

volume = {16},

number = {4},

pages = {329-332},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{10697219,

title = {ISCA: Intelligent Sense-Compute Adaptive Co-optimization of Multimodal Machine Learning Kernels for Resilient mHealth Services on Wearables},

author = {Hamidreza Alikhani and Anil Kanduri and Emad Kasaeyan Naeini and Sina Shahhosseini and Pasi Liljeberg and Amir M. Rahmani and Nikil Dutt},

doi = {10.1109/MDAT.2024.3469828},

year  = {2024},

date = {2024-01-01},

urldate = {2024-01-01},

journal = {IEEE Design & Test},

pages = {1-1},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{10.1145/3616019,

title = {A Deep Learning-Based PPG Quality Assessment Approach for Heart Rate and Heart Rate Variability},

author = {Emad Kasaeyan Naeini and Fatemeh Sarhaddi and Iman Azimi and Pasi Liljeberg and Nikil Dutt and Amir M Rahmani},

url = {https://doi.org/10.1145/3616019},

doi = {10.1145/3616019},

issn = {2691-1957},

year  = {2023},

date = {2023-08-01},

journal = {ACM Trans. Comput. Healthcare},

publisher = {Association for Computing Machinery},

address = {New York, NY, USA},

abstract = {Photoplethysmography (PPG) is a non-invasive optical method to acquire various vital signs, including heart rate (HR) and heart rate variability (HRV). The PPG method is highly susceptible to motion artifacts and environmental noise. Unfortunately, such artifacts are inevitable in ubiquitous health monitoring, as the users are involved in various activities in their daily routines. Such low-quality PPG signals negatively impact the accuracy of the extracted health parameters, leading to inaccurate decision-making. PPG-based health monitoring necessitates a quality assessment approach to determine the signal quality according to the accuracy of the health parameters. Different studies have thus far introduced PPG signal quality assessment methods, exploiting various indicators and machine learning algorithms. These methods differentiate reliable and unreliable signals, considering morphological features of the PPG signal and focusing on the cardiac cycles. Therefore, they can be utilized for HR detection applications. However, they do not apply to HRV, as only having an acceptable shape is insufficient, and other signal factors may also affect the accuracy. In this paper, we propose a deep learning-based PPG quality assessment method for HR and various HRV parameters. We employ one customized one-dimensional (1D) and three two-dimensional (2D) Convolutional Neural Networks (CNN) to train models for each parameter. Reliability of each of these parameters will be evaluated against the corresponding electrocardiogram signal, using 210 hours of data collected from a home-based health monitoring application. Our results show that the proposed 1D CNN method outperforms the other 2D CNN approaches. Our 1D CNN model obtains the accuracy of 95.63%, 96.71%, 91.42%, 94.01%, and 94.81% for the HR, average of normal to normal interbeat (NN) intervals (AVNN), root mean square of successive NN interval differences (RMSSD), standard deviation of NN intervals (SDNN), and ratio of absolute power in low frequency to absolute power in high frequency (LF/HF) ratios, respectively. Moreover, we compare the performance of our proposed method with state-of-the-art algorithms. We compare our best models for HR-HRV health parameters with six different state-of-the-art PPG signal quality assessment methods. Our results indicate that the proposed method performs better than the other methods. We also provide the open-source model implemented in Python for the community to be integrated into their solutions.},

note = {Just Accepted},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{10.1145/3570503,

title = {EASYR: Energy-Efficient Adaptive System Reconfiguration for Dynamic Deadlines in Autonomous Driving on Multicore Processors},

author = {Saehanseul Yi and Tae-Wook Kim and Jong-Chan Kim and Nikil Dutt},

url = {https://doi.org/10.1145/3570503},

doi = {10.1145/3570503},

issn = {1539-9087},

year  = {2023},

date = {2023-04-01},

journal = {ACM Trans. Embed. Comput. Syst.},

volume = {22},

number = {3},

publisher = {Association for Computing Machinery},

address = {New York, NY, USA},

abstract = {The increasing computing demands of autonomous driving applications have driven the adoption of multicore processors in real-time systems, which in turn renders energy optimizations critical for reducing battery capacity and vehicle weight. A typical energy optimization method targeting traditional real-time systems finds a critical speed under a static deadline, resulting in conservative energy savings that are unable to exploit dynamic changes in the system and environment. We capture emerging dynamic deadlines arising from the vehicle’s change in velocity and driving context for an additional energy optimization opportunity. In this article, we extend the preliminary work for uniprocessors [66] to multicore processors, which introduces several challenges. We use the state-of-the-art real-time gang scheduling [5] to mitigate some of the challenges. However, it entails an NP-hard combinatorial problem in that tasks need to be grouped into gangs of tasks, gang formation, which could significantly affect the energy saving result. As such, we present EASYR, an adaptive system optimization and reconfiguration approach that generates gangs of tasks from a given directed acyclic graph for multicore processors and dynamically adapts the scheduling parameters and processor speeds to satisfy dynamic deadlines while consuming as little energy as possible. The timing constraints are also satisfied between system reconfigurations through our proposed safe mode change protocol. Our extensive experiments with randomly generated task graphs show that our gang formation heuristic performs 32% better than the state-of-the-art one. Using an autonomous driving task set from Bosch and real-world driving data, our experiments show that EASYR achieves energy reductions of up to 30.3% on average in typical driving scenarios compared with a conventional energy optimization method with the current state-of-the-art gang formation heuristic in real-time systems, demonstrating great potential for dynamic energy optimization gains by exploiting dynamic deadlines.},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{10286166,

title = {SAFER: Safety Assurances For Emergent Behavior},

author = {Caio Batista De Melo and Marzieh Ashrafiamiri and Minjun Seo and Fadi Kurdahi and Nikil Dutt},

doi = {10.1109/MDAT.2023.3324887},

year  = {2023},

date = {2023-01-01},

journal = {IEEE Design & Test},

pages = {1-1},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{Yang2023.06.08.23291165,

title = {Loneliness Forecasting Using Multi-modal Wearable and Mobile Sensing in Everyday Settings},

author = {Zhongqi Yang and Iman Azimi and Salar Jafarlou and Sina Labbaf and Jessica Borelli and Nikil Dutt and Amir M Rahmani},

url = {https://www.medrxiv.org/content/early/2023/06/12/2023.06.08.23291165},

doi = {10.1101/2023.06.08.23291165},

year  = {2023},

date = {2023-01-01},

journal = {medRxiv},

publisher = {Cold Spring Harbor Laboratory Press},

abstract = {The adverse effects of loneliness on both physical and mental well-being are profound. Although previous research has utilized mobile sensing techniques to detect mental health issues, few studies have utilized state-of-the-art wearable devices to forecast loneliness and comprehend the physiological manifestations of loneliness and its predictive nature. The primary objective of this study is to examine the feasibility of forecasting loneliness by employing wearable devices, such as smart rings and watches, to monitor early physiological indicators of loneliness. Furthermore, smartphones are employed to capture initial behavioral signs of loneliness. To accomplish this, we employed personalized machine learning techniques, leveraging a comprehensive dataset comprising physiological and behavioral information obtained during our study involving the monitoring of college students. Through the development of personalized models, we achieved a notable accuracy of 0.82 and an F-1 score of 0.82 in forecasting loneliness levels seven days in advance. Additionally, the application of Shapley values facilitated model explainability. The wealth of data provided by this study, coupled with the forecasting methodology employed, possesses the potential to augment interventions and facilitate the early identification of loneliness within populations at risk.Competing Interest StatementThe authors have declared no competing interest.Funding StatementThis study did not receive any fundingAuthor DeclarationsI confirm all relevant ethical guidelines have been followed, and any necessary IRB and/or ethics committee approvals have been obtained.YesThe details of the IRB/oversight body that provided approval or exemption for the research described are given below:The Ethics Institutional Review Board of the University of California Irvine gave ethical approval for this work (2019-5153).I confirm that all necessary patient/participant consent has been obtained and the appropriate institutional forms have been archived, and that any patient/participant/sample identifiers included were not known to anyone (e.g., hospital staff, patients or participants themselves) outside the research group so cannot be used to identify individuals.YesI understand that all clinical trials and any other prospective interventional studies must be registered with an ICMJE-approved registry, such as ClinicalTrials.gov. I confirm that any such study reported in the manuscript has been registered and the trial registration ID is provided (note: if posting a prospective study registered retrospectively, please provide a statement in the trial ID field explaining why the study was not registered in advance).YesI have followed all appropriate research reporting guidelines, such as any relevant EQUATOR Network research reporting checklist(s) and other pertinent material, if applicable.YesAll data produced in the present study are available upon reasonable request to the authors},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{Shahhosseini2022,

title = {Exploring computation offloading in IoT systems},

author = {Sina Shahhosseini and Arman Anzanpour and Iman Azimi and Sina Labbaf and DongJoo Seo and Sung-Soo Lim and Pasi Liljeberg and Nikil Dutt and Amir M Rahmani },

url = {https://www.sciencedirect.com/science/article/pii/S0306437921000910?via%3Dihub},

issn = {0306-4379},

year  = {2022},

date = {2022-07-01},

journal = {Information Systems},

volume = {107},

abstract = {Internet of Things (IoT) paradigm raises challenges for devising efficient strategies that offload

applications to the fog or the cloud layer while ensuring the optimal response time for a ser-

vice. Traditional computation offloading policies assume the response time is only dominated by

the execution time. However, the response time is a function of many factors including contextual

parameters and application characteristics that can change over time. For the computation offloading

problem, the majority of existing literature presents efficient solutions considering a limited number of

parameters (e.g., computation capacity and network bandwidth) neglecting the effect of the application

characteristics and dataflow configuration. In this paper, we explore the impact of the computation

offloading on total application response time in three-layer IoT systems considering more realistic

parameters, e.g., application characteristics, system complexity, communication cost, and dataflow

configuration. This paper also highlights the impact of a new application characteristic parameter

defined as Output–Input Data Generation (OIDG) ratio and dataflow configuration on the system

behavior. In addition, we present a proof-of-concept end-to-end dynamic computation offloading

technique, implemented in a real hardware setup, that observes the aforementioned parameters to

perform real-time decision-making.},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{https://doi.org/10.1145/3520129,

title = {Online Learning for Orchestration of Inference in Multi-User End-Edge-Cloud Networks},

author = {Sina Shahhosseini and Dongjoo Seo and Anil Kanduri and Tianyi Hu and Sung-Soo Lim and Bryan Donyanavard and Amir M. Rahmani and Nikil D. Dutt},

url = {https://dl.acm.org/doi/abs/10.1145/3520129},

doi = {10.1145/3520129},

issn = {1539-9087},

year  = {2022},

date = {2022-02-21},

journal = {ACM Transactions on Embedded Computing Systems (TECS)},

abstract = {Deep-learning-based intelligent services have become prevalent in cyber-physical applications including smart cities and health-care. Deploying deep-learning-based intelligence near the end-user enhances privacy protection, responsiveness, and reliability. Resource-constrained end-devices must be carefully managed in order to meet the latency and energy requirements of computationally-intensive deep learning services. Collaborative end-edge-cloud computing for deep learning provides a range of performance and efficiency that can address application requirements through computation offloading. The decision to offload computation is a communication-computation co-optimization problem that varies with both system parameters (e.g., network condition) and workload characteristics (e.g., inputs). On the other hand, deep learning model optimization provides another source of tradeoff between latency and model accuracy. An end-to-end decision-making solution that considers such computation-communication problem is required to synergistically find the optimal offloading policy and model for deep learning services. To this end, we propose a reinforcement-learning-based computation offloading solution that learns optimal offloading policy considering deep learning model selection techniques to minimize response time while providing sufficient accuracy. We demonstrate the effectiveness of our solution for edge devices in an end-edge-cloud system and evaluate with a real-setup implementation using multiple AWS and ARM core configurations. Our solution provides 35% speedup in the average response time compared to the state-of-the-art with less than 0.9% accuracy reduction, demonstrating the promise of our online learning framework for orchestrating DL inference in end-edge-cloud systems.},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{DBLP:journals/corr/abs-2103-05707,

title = {Endurance-Aware Mapping of Spiking Neural Networks to Neuromorphic 

 Hardware},

author = {Twisha Titirsha and Shihao Song and Anup Das and Jeffrey L Krichmar and Nikil D Dutt and Nagarajan Kandasamy and Francky Catthoor},

url = {https://arxiv.org/abs/2103.05707},

year  = {2021},

date = {2021-01-01},

journal = {CoRR},

volume = {abs/2103.05707},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{DBLP:journals/corr/abs-2105-01795,

title = {NeuroXplorer 1.0: An Extensible Framework for Architectural Exploration 

 with Spiking Neural Networks},

author = {Adarsha Balaji and Shihao Song and Twisha Titirsha and Anup Das and Jeffrey L Krichmar and Nikil D Dutt and James A Shackleford and Nagarajan Kandasamy and Francky Catthoor},

url = {https://arxiv.org/abs/2105.01795},

year  = {2021},

date = {2021-01-01},

journal = {CoRR},

volume = {abs/2105.01795},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{DBLP:journals/corr/abs-2105-02038,

title = {Dynamic Reliability Management in Neuromorphic Computing},

author = {Shihao Song and Jui Hanamshet and Adarsha Balaji and Anup Das and Jeffrey L Krichmar and Nikil D Dutt and Nagarajan Kandasamy and Francky Catthoor},

url = {https://arxiv.org/abs/2105.02038},

year  = {2021},

date = {2021-01-01},

journal = {CoRR},

volume = {abs/2105.02038},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{DBLP:journals/corr/abs-2107-11417,

title = {MARS: Middleware for Adaptive Reflective Computer Systems},

author = {Tiago Mück and Bryan Donyanavard and Biswadip Maity and Kasra Moazzemi and Nikil D Dutt},

url = {https://arxiv.org/abs/2107.11417},

year  = {2021},

date = {2021-01-01},

journal = {CoRR},

volume = {abs/2107.11417},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{DBLP:journals/corr/abs-2108-00144,

title = {Personalized Stress Monitoring using Wearable Sensors in Everyday 

 Settings},

author = {Ali Tazarv and Sina Labbaf and Stephanie M Reich and Nikil D Dutt and Amir M Rahmani and Marco Levorato},

url = {https://arxiv.org/abs/2108.00144},

year  = {2021},

date = {2021-01-01},

journal = {CoRR},

volume = {abs/2108.00144},

keywords = {},

pubstate = {published},

tppubtype = {article}

}