2024 |
Chen, Ping-Xiang; Seo, Dongjoo; Sung, Changhoon; Park, Jongheum; Lee, Minchul; Li, Huaicheng; Bjørling, Matias; Dutt, Nikil ZoneTrace: A Zone Monitoring Tool for F2FS on ZNS SSDs Journal Article In: ACM Trans. Des. Autom. Electron. Syst., 2024, ISSN: 1084-4309, (Just Accepted). @article{10.1145/3656172,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. |
Seo, Dongjoo; Joo, Yongsoo; Dutt, Nikil Improving Virtualized I/O Performance by Expanding the Polled I/O Path of Linux Proceedings Article In: Proceedings of the 16th ACM Workshop on Hot Topics in Storage and File Systems, pp. 31–37, Association for Computing Machinery, Santa Clara, CA, USA, 2024, ISBN: 9798400706301. @inproceedings{10.1145/3655038.3665944,The continuing advancement of storage technology has introduced ultra-low latency (ULL) SSDs that feature 20 μs or less access latency. Therefore, the context switching overhead of interrupts has become more pronounced on these SSDs, prompting consideration of polling as an alternative to mitigate this overhead. At the same time, the high price of ULL SSDs is a major issue preventing the wide adoption of polling.We claim that virtualized systems can benefit from polling even without ULL SSDs. Since the host page cache is located in the DRAM main memory, it can deliver even higher throughput than ULL SSDs. However, the guest operating system in virtualized environments cannot use polled I/Os when accessing the host page cache, failing to exploit the performance advantage of DRAM. To resolve this inefficiency, we propose to expand the polled I/O path of the Linux kernel I/O stack. Our approach allows guest applications to use I/O polling for buffered I/Os and memory mapped I/Os. The expanded I/O path can significantly improve the I/O performance of virtualized systems without modifying the guest application or the backend of the virtual block device. Our proposed buffered I/O path with polling improves the 4 KB random read throughput between guest applications and the host page cache by 3.2×. |
Chen, Ping-Xiang; Seo, Dongjoo; Maity, Biswadip; Dutt, Nikil KDTree-SOM: Self-organizing Map based Anomaly Detection for Lightweight Autonomous Embedded Systems Proceedings Article In: Proceedings of the Great Lakes Symposium on VLSI 2024, pp. 700–705, Association for Computing Machinery, Clearwater, FL, USA, 2024, ISBN: 9798400706059. @inproceedings{10.1145/3649476.3658708,Self-Organizing Maps (SOM) promise a lightweight approach for multivariate time series anomaly detection in lightweight autonomous embedded systems. However, the enormous volume of time series data from autonomous systems testing requires huge SOMs with impractical search overhead. We present KDTree-SOM that effectively optimizes the winner node search for huge SOMs by reconstructing the SOM as a k-dimensional tree (kd-tree). KDTree-SOM achieves on average a 4 × inference time reduction for huge SOMs while achieving up to 95% anomaly detection accuracy with only KB-level memory overhead, demonstrating its potential for anomaly detection in lightweight autonomous embedded platforms. |
Alikhani, Hamidreza; Wang, Ziyu; Kanduri, Anil; Lilieberg, Pasi; Rahmani, Amir M.; Dutt, Nikil SEAL: Sensing Efficient Active Learning on Wearables through Context-awareness Proceedings Article In: 2024 Design, Automation & Test in Europe Conference & Exhibition (DATE), pp. 1-2, 2024. @inproceedings{10546533, |
Abraham, Danny; Maity, Biswadip; Donyanavard, Bryan; Dutt, Nikil Back to the Future: Reversible Runtime Neural Network Pruning for Safe Autonomous Systems Proceedings Article In: 2024 Design, Automation & Test in Europe Conference & Exhibition (DATE), pp. 1-6, 2024, ISBN: 978-3-9819263-8-5. @inproceedings{10546571, |
Yi, Saehanseul; Dutt, Nikil BoostIID: Fault-agnostic Online Detection of WCET Changes in Autonomous Driving Proceedings Article In: 2024 29th Asia and South Pacific Design Automation Conference (ASP-DAC), pp. 704-709, 2024. @inproceedings{10473866, |
2023 |
Naeini, Emad Kasaeyan; Sarhaddi, Fatemeh; Azimi, Iman; Liljeberg, Pasi; Dutt, Nikil; Rahmani, Amir M A Deep Learning-Based PPG Quality Assessment Approach for Heart Rate and Heart Rate Variability Journal Article In: ACM Trans. Comput. Healthcare, 2023, ISSN: 2691-1957, (Just Accepted). @article{10.1145/3616019,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. |
Yi, Saehanseul; Kim, Tae-Wook; Kim, Jong-Chan; Dutt, Nikil EASYR: Energy-Efficient Adaptive System Reconfiguration for Dynamic Deadlines in Autonomous Driving on Multicore Processors Journal Article In: ACM Trans. Embed. Comput. Syst., vol. 22, no. 3, 2023, ISSN: 1539-9087. @article{10.1145/3570503,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. |
Alikhani, Hamidreza; Kanduri, Anil; Liljeberg, Pasi; Rahmani, Amir M; Dutt, Nikil DynaFuse: Dynamic Fusion for Resource Efficient Multi-Modal Machine Learning Inference Conference 2023, ISSN: 1943-0671. @conference{10261977,Multi-modal machine learning (MMML) applications combine results from different modalities in the inference phase to improve prediction accuracy. Existing MMML fusion strategies use static modality weight assignment, based on the intrinsic value of sensor modalities determined during the training phase. However, input data perturbations in practical scenarios affect the intrinsic value of modalities in the inference phase, lowering prediction accuracy, and draining computational and energy resources. In this work, we present DynaFuse, a framework for dynamic and adaptive fusion of MMML inference to set modality weights, considering run-time parameters of input data quality and sensor energy budgets. We determine the insightfulness of modalities by combining design-time intrinsic value with the run-time extrinsic value of different modalities to assign updated modality weights, catering to both accuracy requirements and energy conservation demands. The DynaFuse approach achieves up to 22% gain in prediction accuracy and an average energy savings of 34% on exemplary MMML applications of human activity recognition and stress monitoring in comparison with state-of-the-art static fusion approaches. |
de Melo, Caio Batista; Dutt, Nikil LOCoCAT: Low-Overhead Classification of CAN Bus Attack Types Conference 2023. @conference{10261979, |
Melo, Caio Batista De; Ashrafiamiri, Marzieh; Seo, Minjun; Kurdahi, Fadi; Dutt, Nikil SAFER: Safety Assurances For Emergent Behavior Journal Article In: IEEE Design & Test, pp. 1-1, 2023. @article{10286166, |
Seo, Dongjoo; Chen, Ping-Xiang; Li, Huaicheng; Bjørling, Matias; Dutt, Nikil Is Garbage Collection Overhead Gone? Case Study of F2FS on ZNS SSDs Proceedings Article In: Proceedings of the 15th ACM Workshop on Hot Topics in Storage and File Systems, pp. 102–108, Association for Computing Machinery, Boston, MA, USA, 2023, ISBN: 9798400702242. @inproceedings{10.1145/3599691.3603409,The sequential write nature of ZNS SSDs makes them very well-suited for log-structured file systems. The Flash-Friendly File System (F2FS), is one such log-structured file system and has recently gained support for use with ZNS SSDs. The large F2FS over-provisioning space for ZNS SSDs greatly reduces the garbage collection (GC) overhead in the log-structured file systems. Motivated by this observation, we explore the trade-off between disk utilization and over-provisioning space, which affects the garbage collection process, as well as the user application performance. To address the performance degradation in write-intensive workloads caused by GC overhead, we propose a modified free segment-finding policy and a Parallel Garbage Collection (P-GC) scheme for F2FS that efficiently reduces GC overhead. Our evaluation results demonstrate that our P-GC scheme can achieve up to 42% performance enhancement with various workloads. |
Bhattacharjya, Rajat; Maity, Biswadip; Dutt, Nikil Locate: Low-Power Viterbi Decoder Exploration Using Approximate Adders Proceedings Article In: Proceedings of the Great Lakes Symposium on VLSI 2023, pp. 409–413, Association for Computing Machinery, Knoxville, TN, USA, 2023, ISBN: 9798400701252. @inproceedings{10.1145/3583781.3590314,Viterbi decoders are widely used in communication systems, natural language processing (NLP), and other domains. While Viterbi decoders are compute-intensive and power-hungry, we can exploit approximations for early design space exploration (DSE) of trade-offs between accuracy, power, and area. We present Locate, a DSE framework that uses approximate adders in the critically compute and power-intensive Add-Compare-Select Unit (ACSU) of the Viterbi decoder. We demonstrate the utility of Locate for early DSE of accuracy-power-area trade-offs for two applications: communication systems and NLP, showing a range of pareto-optimal design configurations. For instance, in the communication system, using an approximate adder, we observe savings of 21.5% area and 31.02% power with only 0.142% loss in accuracy averaged across three modulation schemes. Similarly, for a Parts-of-Speech Tagger in an NLP setting, out of 15 approximate adders, 7 report 100% accuracy while saving 22.75% area and 28.79% power on average when compared to using a Carry-Lookahead Adder in the ACSU. These results show that Locate can be used synergistically with other optimization techniques to improve the end-to-end efficiency of Viterbi decoders for various application domains. |
Esterle, Lukas; Dutt, Nikil; Gruhl, Christian; Lewis, Peter R; Marcenaro, Lucio; Regazzoni, Carlo; Jantsch, Axel Self-awareness in Cyber-Physical Systems: Recent Developments and Open Challenges Proceedings Article In: 2023 Design, Automation & Test in Europe Conference & Exhibition (DATE), pp. 1-6, 2023. @inproceedings{10137197, |
Yang, Zhongqi; Azimi, Iman; Jafarlou, Salar; Labbaf, Sina; Borelli, Jessica; Dutt, Nikil; Rahmani, Amir M Loneliness Forecasting Using Multi-modal Wearable and Mobile Sensing in Everyday Settings Journal Article In: medRxiv, 2023. @article{Yang2023.06.08.23291165,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 |
Sperling, Nora; Bendrick, Alex; Stöhrmann, Dominik; Ernst, Rolf; Donyanavard, Bryan; Maurer, Florian; Lenke, Oliver; Surhonne, Anmol; Herkersdorf, Andreas; Amer, Walaa; de Melo, Caio Batista; Chen, Ping-Xiang; Hoang, Quang Anh; Karami, Rachid; Maity, Biswadip; Nikolian, Paul; Rakka, Mariam; Seo, Dongjoo; Yi, Saehanseul; Seo, Minjun; Dutt, Nikil; Kurdahi, Fadi Information Processing Factory 2.0 - Self-awareness for Autonomous Collaborative Systems Proceedings Article In: 2023 Design, Automation & Test in Europe Conference & Exhibition (DATE), pp. 1-6, 2023. @inproceedings{10137006, |
2022 |
Ji, Mingoo; Yi, Saehanseul; Koo, Changjin; Ahn, Sol; Seo, Dongjoo; Dutt, Nikil; Kim, Jong-Chan Demand Layering for Real-Time DNN Inference with Minimized Memory Usage Proceedings Article In: 2022 IEEE Real-Time Systems Symposium (RTSS), pp. 291-304, 2022. @inproceedings{9984745,When executing a deep neural network (DNN), its model parameters are loaded into GPU memory before execution, incurring a significant GPU memory burden. There are studies that reduce GPU memory usage by exploiting CPU memory as a swap device. However, this approach is not applicable in most embedded systems with integrated GPUs where CPU and GPU share a common memory. In this regard, we present Demand Layering, which employs a fast solid-state drive (SSD) as a co-running partner of a GPU and exploits the layer-by-layer execution of DNNs. In our approach, a DNN is loaded and executed in a layer-by-layer manner, minimizing the memory usage to the order of a single layer. Also, we developed a pipeline architecture that hides most additional delays caused by the interleaved parameter loadings alongside layer executions. Our implementation shows a 96.5% memory reduction with just 14.8% delay overhead on average for representative DNNs. Furthermore, by exploiting the memory-delay tradeoff, near-zero delay overhead (under 1 ms) can be achieved with a slightly increased memory usage (still an 88.4% reduction), showing the great potential of Demand Layering. |
Seo, Dongjoo; Maity, Biswadip; Chen, Ping-Xiang; Yun, Dukyoung; Donyanavard, Bryan; Dutt, Nikil ProSwap: Period-aware Proactive Swapping to Maximize Embedded Application Performance Proceedings Article In: 2022 IEEE International Conference on Networking, Architecture and Storage (NAS), pp. 1-4, 2022. @inproceedings{9925330,Linux prevents errors due to physical memory limits by swapping out active application memory from main memory to secondary storage. Swapping degrades application performance due to swap-in/out latency overhead. To mitigate the swapping overhead in periodic applications, we present ProSwap: a period-aware proactive and adaptive swapping policy for embedded systems. ProSwap exploits application periodic behavior to proactively swap-out rarely-used physical memory pages, creating more space for active processes. A flexible memory reclamation time window enables adaptation to memory limitations that vary between applications. We demonstrate ProSwap's efficacy for an autonomous vehicle application scenario executing multi-application pipelines and show that our policy achieves up to 1.26×performance gain via proactive swapping. |
Shahhosseini, Sina; Anzanpour, Arman; Azimi, Iman; Labbaf, Sina; Seo, DongJoo; Lim, Sung-Soo; Liljeberg, Pasi; Dutt, Nikil; Rahmani, Amir M Exploring computation offloading in IoT systems Journal Article In: Information Systems, vol. 107, 2022, ISSN: 0306-4379. @article{Shahhosseini2022,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. |
Shahhosseini, Sina; Hu, Tianyi; Seo, Dongjoo; Kanduri, Anil; Donyanavard, Bryan; Rahmani, Amir M; Dutt, Nikil Hybrid Learning for Orchestrating Deep Learning Inference in Multi-user Edge-cloud Networks (Best Paper Award) Proceedings Article In: 2022 23rd International Symposium on Quality Electronic Design (ISQED), pp. 1-6, 2022. @inproceedings{9806291,Deep-learning-based intelligent services have become prevalent in cyber-physical applications including smart cities and health-care. 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). Identifying optimal orchestration considering the cross-layer opportunities and requirements in the face of varying system dynamics is a challenging multi-dimensional problem. While Reinforcement Learning (RL) approaches have been proposed earlier, they suffer from a large number of trial-and-errors during the learning process resulting in excessive time and resource consumption. We present a Hybrid Learning orchestration framework that reduces the number of interactions with the system environment by combining model-based and model-free reinforcement learning. Our Deep Learning inference orchestration strategy employs reinforcement learning to find the optimal orchestration policy. Furthermore, we deploy Hybrid Learning (HL) to accelerate the RL learning process and reduce the number of direct samplings. We demonstrate efficacy of our HL strategy through experimental comparison with state-of-the-art RL-based inference orchestration, demonstrating that our HL strategy accelerates the learning process by up to 166.6×. |
Shahhossein, Sina; Ni, Yang; Naeini, Emad Kasaeyan; Imani, Mohsen; Rahmani, Amir M.; Dutt, Nikil Flexible and Personalized Learning for Wearable Health Applications using HyperDimensional Computing Proceedings Proceedings of the Great Lakes Symposium on VLSI , 2022, ISBN: 9781450393225. @proceedings{Shahhossein2022,Health and wellness applications increasingly rely on machine learning techniques to learn end-user physiological and behavioral patterns in everyday settings, posing two key challenges: inability to perform on-device online learning for resource-constrained wearables, and learning algorithms that support privacy-preserving personalization. We exploit a Hyperdimensional computing (HDC) solution for wearable devices that offers flexibility, high efficiency, and performance while enabling on-device personalization and privacy protection. We evaluate the efficacy of our approach using three case studies and show that our system improves performance of training by up to 35.8x compared with the state-of-the-art while offering a comparable accurac |
Naeini, Emad Kasaeyan; Shahhosseini, Sina; Kanduri, Anil; Liljeberg, Pasi; Rahmani, Amir M.; Dutt, Nikil AMSER: Adaptive Multimodal Sensing for Energy Efficient and Resilient eHealth Systems Conference 2022 Design, Automation Test in Europe Conference Exhibition (DATE), 2022, ISBN: 978-3-9819263-6-1. @conference{Naeini2022,eHealth systems deliver critical digital healthcare and wellness services for users by continuously monitoring physiological and contextual data. eHealth applications use multi-modal machine learning kernels to analyze data from different sensor modalities and automate decision-making. Noisy inputs and motion artifacts during sensory data acquisition affect the i) prediction accuracy and resilience of eHealth services and ii) energy efficiency in processing garbage data. Monitoring raw sensory inputs to identify and drop data and features from noisy modalities can improve prediction accuracy and energy efficiency. We propose a closed-loop monitoring and control framework for multi-modal eHealth applications, AMSER, that can mitigate garbage-in garbage-out by i) monitoring input modalities, ii) analyzing raw input to selectively drop noisy data and features, and iii) choosing appropriate machine learning models that fit the configured data and feature vector - to improve prediction accuracy and energy efficiency. We evaluate our AMSER approach using multi-modal eHealth applications of pain assessment and stress monitoring over different levels and types of noisy components incurred via different sensor modalities. Our approach achieves up to 22% improvement in prediction accuracy and 5.6× energy consumption reduction in the sensing phase against the state-of-the-art multi-modal monitoring application. |
Vo, Khuong; Vishwanath, Manoj; Srinivasan, Ramesh; Dutt, Nikil; Cao, Hung Composing Graphical Models with Generative Adversarial Networks for EEG Signal Modeling Conference Composing Graphical Models with Generative Adversarial Networks for EEG Signal Modeling, 2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), 2022, ISSN: 2379-190X. @conference{Vo2022,Neural oscillations in the form of electroencephalogram (EEG) can reveal underlying brain functions, such as cognition, memory, perception, and consciousness. A comprehensive EEG computational model provides not only a stochastic procedure that directly generates data but also insights to further understand the neurological mechanisms. Here, we propose a generative and inference approach that combines the complementary benefits of probabilistic graphical models and generative adversarial networks (GANs) for EEG signal modeling. We investigate the method’s ability to jointly learn coherent generation and inverse inference models on the CHIMIT epilepsy multi-channel EEG dataset. We further study the efficacy of the learned representations in epilepsy seizure detection formulated as an unsupervised learning problem. Quantitative and qualitative experimental results demonstrate the effectiveness and efficiency of our approach. |
Shahhosseini, Sina; Seo, Dongjoo; Kanduri, Anil; Hu, Tianyi; Lim, Sung-Soo; Donyanavard, Bryan; Rahmani, Amir M.; Dutt, Nikil D. Online Learning for Orchestration of Inference in Multi-User End-Edge-Cloud Networks Journal Article In: ACM Transactions on Embedded Computing Systems (TECS), 2022, ISSN: 1539-9087. @article{https://doi.org/10.1145/3520129,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. |
2021 |
Aqajari, Seyed Amir Hossein; Naeini, Emad Kasaeyan; Mehrabadi, Milad Asgari; Labbaf, Sina; Dutt, Nikil D; Rahmani, Amir M pyEDA: An Open-Source Python Toolkit for Pre-processing and Feature Extraction of Electrodermal Activity Proceedings Article In: The 12th International Conference on Ambient Systems, Networks and Technologies (ANT 2021) / The 4th International Conference on Emerging Data and Industry 4.0 (EDI40 2021) / Affiliated Workshops, March 23-26, 2021, Warsaw, Poland, pp. 99–106, Elsevier, 2021. @inproceedings{DBLP:conf/ant/AqajariNMLDR21, |
Vo, Khuong; Naeini, Emad Kasaeyan; Naderi, Amir; Jilani, Daniel; Rahmani, Amir M; Dutt, Nikil D; Cao, Hung P2E-WGAN: ECG waveform synthesis from PPG with conditional wasserstein generative adversarial networks Proceedings Article In: SAC '21: The 36th ACM/SIGAPP Symposium on Applied Computing, Virtual Event, Republic of Korea, March 22-26, 2021, pp. 1030–1036, ACM, 2021. @inproceedings{DBLP:conf/sac/VoNNJRDC21, |
Donyanavard, Bryan; ü, Tiago M; Moazzemi, Kasra; Maity, Biswadip; de Melo, Caio Batista; Stewart, Kenneth; Yi, Saehanseul; Rahmani, Amir M; Dutt, Nikil D Reflecting on Self-Aware Systems-on-Chip Proceedings Article In: A Journey of Embedded and Cyber-Physical Systems - Essays Dedicated to Peter Marwedel on the Occasion of His 70th Birthday, pp. 79–95, Springer, 2021. @inproceedings{DBLP:conf/birthday/DonyanavardMMMM21, |
Tazarv, Ali; Labbaf, Sina; Rahmani, Amir M; Dutt, Nikil D; Levorato, Marco Data Collection and Labeling of Real-Time IoT-Enabled Bio-Signals in Everyday Settings for Mental Health Improvement Proceedings Article In: Gaggi, Ombretta; Manzoni, Pietro; Palazzi, Claudio E (Ed.): GoodIT '21: Conference on Information Technology for Social Good, Roma, Italy, September 9-11, 2021, pp. 186–191, ACM, 2021. @inproceedings{DBLP:conf/goodit/TazarvLRDL21, |
Yi, Saehanseul; Kim, Tae-Wook; Kim, Jong-Chan; Dutt, Nikil D Energy-Efficient Adaptive System Reconfiguration for Dynamic Deadlines in Autonomous Driving Proceedings Article In: 24th IEEE International Symposium on Real-Time Distributed Computing, ISORC 2021, Daegu, South Korea, June 1-3, 2021, pp. 96–104, IEEE, 2021. @inproceedings{DBLP:conf/isorc/YiKKD21, |
Titirsha, Twisha; Song, Shihao; Das, Anup; Krichmar, Jeffrey L; Dutt, Nikil D; Kandasamy, Nagarajan; Catthoor, Francky Endurance-Aware Mapping of Spiking Neural Networks to Neuromorphic Hardware Journal Article In: CoRR, vol. abs/2103.05707, 2021. @article{DBLP:journals/corr/abs-2103-05707, |
Balaji, Adarsha; Song, Shihao; Titirsha, Twisha; Das, Anup; Krichmar, Jeffrey L; Dutt, Nikil D; Shackleford, James A; Kandasamy, Nagarajan; Catthoor, Francky NeuroXplorer 1.0: An Extensible Framework for Architectural Exploration with Spiking Neural Networks Journal Article In: CoRR, vol. abs/2105.01795, 2021. @article{DBLP:journals/corr/abs-2105-01795, |
Song, Shihao; Hanamshet, Jui; Balaji, Adarsha; Das, Anup; Krichmar, Jeffrey L; Dutt, Nikil D; Kandasamy, Nagarajan; Catthoor, Francky Dynamic Reliability Management in Neuromorphic Computing Journal Article In: CoRR, vol. abs/2105.02038, 2021. @article{DBLP:journals/corr/abs-2105-02038, |
Mück, Tiago; Donyanavard, Bryan; Maity, Biswadip; Moazzemi, Kasra; Dutt, Nikil D MARS: Middleware for Adaptive Reflective Computer Systems Journal Article In: CoRR, vol. abs/2107.11417, 2021. @article{DBLP:journals/corr/abs-2107-11417, |
Tazarv, Ali; Labbaf, Sina; Reich, Stephanie M; Dutt, Nikil D; Rahmani, Amir M; Levorato, Marco Personalized Stress Monitoring using Wearable Sensors in Everyday Settings Journal Article In: CoRR, vol. abs/2108.00144, 2021. @article{DBLP:journals/corr/abs-2108-00144, |
Maity, Biswadip; Donyanavard, Bryan; Surhonne, Anmol; Rahmani, Amir M; Herkersdorf, Andreas; Dutt, Nikil D SEAMS: Self-Optimizing Runtime Manager for Approximate Memory Hierarchies Journal Article In: ACM Trans. Embed. Comput. Syst., vol. 20, no. 5, pp. 48:1–48:26, 2021. @article{DBLP:journals/tecs/MaityDSRHD21, |
Sarhaddi, Fatemeh; Azimi, Iman; Labbaf, Sina; Niela-Vilén, Hannakaisa; Dutt, Nikil D; Axelin, Anna; Liljeberg, Pasi; Rahmani, Amir M Long-Term IoT-Based Maternal Monitoring: System Design and Evaluation Journal Article In: Sensors, vol. 21, no. 7, pp. 2281, 2021. @article{DBLP:journals/sensors/SarhaddiALNDALR21, |
Shahhosseini, Sina; Kanduri, Anil; Mehrabadi, Milad Asgari; Naeini, Emad Kasaeyan; Seo, Dongjoo; Lim, Sung-Soo; Rahmani, Amir M; Dutt, Nikil D Towards Smart and Efficient Health Monitoring Using Edge-enabled Situational-awareness Proceedings Article In: 3rd IEEE International Conference on Artificial Intelligence Circuits and Systems, AICAS 2021, Washington, DC, USA, June 6-9, 2021, pp. 1–4, IEEE, 2021. @inproceedings{DBLP:conf/aicas/ShahhosseiniKMN21, |
2020 |
Anzanpour, Arman; Amiri, Delaram; Azimi, Iman; Levorato, Marco; Dutt, Nikil; Liljeberg, Pasi; Rahmani, Amir M. Edge-Assisted Control for Healthcare Internet-of-Things: A Case Study on PPG-based Early Warning Score Journal Article In: ACM transaction on Internet of Things , 2020. @article{EdgeControl-ACM2020,Recent advances in pervasive Internet of Things (IoT) technologies and edge computing have opened new avenues for development of ubiquitous health monitoring applications. Delivering an acceptable level of usability and accuracy for these healthcare IoT applications requires optimization of both system-driven and data-driven aspects which are typically done in a disjoint manner. While decoupled optimization of these processes yields local optima at each level, synergistic coupling of the system and data levels can lead to a holistic solution opening new opportunities for optimization. In this paper, we present an edge-assisted resource manager that dynamically controls the delity and duration of sensing w.r.t. changes in the patient's activity and health state, thus ne-tuning the trade-o between energy-e ciency and measurement accuracy. The cornerstone of our proposed solution is an intelligent low-latency real-time controller implemented at the edge layer that detects abnormalities in the patient's condition and accordingly adjusts the sensing parameters of a recon gurable wireless sensor node. We assess the e ciency of our proposed system via a case study of PPG-based medical Early Warning Score (EWS) system. Our experiments on a real full hardware-software EWS system reveal up to 49% power savings while maintaining the accuracy of the sensory data. |
Hamid; Saransh Nejatollahi, Gupta; Imani CryptoPIM: In-memory Acceleration for Lattice-based Cryptographic Hardware Journal Article In: Design Automation Conference (DAC), 2020, (Best paper award finalist). @article{Nejatollahi2020-DAC, |
Amiri, Delaram; Anzanpour, Arman; Azimi, Iman; Rahmani, Amir M; Liljeberg, Pasi; Dutt, Nikil; Levorato, Marco Optimizing Energy Efficiency of Wearable Sensors Using Fog-assisted Control Book Chapter In: Fog Computing: Theory and Practice, Chapter 9, pp. arXiv:1907.11989, Wiley, 2020, ISBN: 9781119551713. @inbook{amiribook2020,Recent advances in the Internet of Things (IoT) technologies have enabled the use of wearables for remote patient monitoring. Wearable sensors capture the patient's vital signs, and provide alerts or diagnosis based on the collected data. Unfortunately, wearables typically have limited energy and computational capacity, making their use challenging for healthcare applications where monitoring must continue uninterrupted long time, without the need to charge or change the battery. Fog computing can alleviate this problem by offloading computationally intensive tasks from the sensor layer to higher layers, thereby not only meeting the sensors' limited computational capacity but also enabling the use of local closed-loop energy optimization algorithms to increase the battery life. |
Lee, Tao-Yi; Vo, Khuong; Baek, Wongi; Khine, Michelle; Dutt, Nikil D STINT: selective transmission for low-energy physiological monitoring Proceedings Article In: ISLPED '20: ACM/IEEE International Symposium on Low Power Electronics and Design, Boston, Massachusetts, August 10-12, 2020, pp. 115–120, ACM, 2020. @inproceedings{DBLP:conf/islped/LeeVBKD20, |
Vo, Khuong; Le, Tai; Rahmani, Amir M; Dutt, Nikil D; Cao, Hung An Efficient and Robust Deep Learning Method with 1-D Octave Convolution to Extract Fetal Electrocardiogram Journal Article In: Sensors, vol. 20, no. 13, pp. 3757, 2020. @article{DBLP:journals/sensors/VoLRDC20, |
Peroni, Daniel; Imani, Mohsen; Hamid, Nejatollahi; Dutt, Nikil; Rosing, Tajana Data Reuse for Accelerated Approximate Warps Journal Article In: IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems (TCAD), 2020. @article{Nejatollahi2020-TCAD, |
Hamid; Sina Shahhosseini; Cammarota Nejatollahi, Rosario; Dutt Exploring Energy Efficient Quantum-resistant Signal Processing Using Array Processors Conference International Conference on Acoustics, Speech, and Signal Processing (ICASSP), 2020, (Best paper award finalist). @conference{Nejatollah-ICASSP, |
Hamid; Felipe Nejatollahi, Valencia; Banik Synthesis of Flexible Accelerators for Early Adoption of Ring-LWE Post-quantum Cryptography Journal Article In: Transactions on Embedded Computing Systems, 2020. @article{Nejatollahi2020-TECS, |
Jantsch, Axel; Lewis, Peter R; Dutt, Nikil D Introduction to the Special Issue on Self-Aware Cyber-physical Systems Journal Article In: ACM Trans. Cyber Phys. Syst., vol. 4, no. 4, pp. 37:1–37:2, 2020. @article{DBLP:journals/tcps/JantschLD20, |
Bellman, Kirstie L; Landauer, Christopher; Dutt, Nikil D; Esterle, Lukas; Herkersdorf, Andreas; Jantsch, Axel; Taherinejad, Nima; Lewis, Peter R; Platzner, Marco; ä, Kalle Tammem Self-aware Cyber-Physical Systems Journal Article In: ACM Trans. Cyber Phys. Syst., vol. 4, no. 4, pp. 38:1–38:26, 2020. @article{DBLP:journals/tcps/BellmanLDEHJTLP20, |
Balaji, Adarsha; Catthoor, Francky; Das, Anup; Wu, Yuefeng; Huynh, Khanh; Dell'Anna, Francesco; Indiveri, Giacomo; Krichmar, Jeffrey L; Dutt, Nikil D; Schaafsma, Siebren Mapping Spiking Neural Networks to Neuromorphic Hardware Journal Article In: IEEE Trans. Very Large Scale Integr. Syst., vol. 28, no. 1, pp. 76–86, 2020. @article{DBLP:journals/tvlsi/BalajiCDWHDIKDS20, |
Williams, Lucretia; Hayes, Gillian R; Guo, Yuqing; Rahmani, Amir; Dutt, Nikil D HCI and mHealth Wearable Tech: A Multidisciplinary Research Challenge Proceedings Article In: Extended Abstracts of the 2020 CHI Conference on Human Factors in Computing Systems, CHI 2020, Honolulu, HI, USA, April 25-30, 2020, pp. 1–7, ACM, 2020. @inproceedings{DBLP:conf/chi/WilliamsHGRD20, |
Maurer, Florian; Donyanavard, Bryan; Rahmani, Amir M; Dutt, Nikil D; Herkersdorf, Andreas Emergent Control of MPSoC Operation by a Hierarchical Supervisor / Reinforcement Learning Approach Proceedings Article In: 2020 Design, Automation & Test in Europe Conference & Exhibition, DATE 2020, Grenoble, France, March 9-13, 2020, pp. 1562–1567, IEEE, 2020. @inproceedings{DBLP:conf/date/MaurerDRDH20, |
Publications
2024 |
ZoneTrace: A Zone Monitoring Tool for F2FS on ZNS SSDs Journal Article In: ACM Trans. Des. Autom. Electron. Syst., 2024, ISSN: 1084-4309, (Just Accepted). |
Improving Virtualized I/O Performance by Expanding the Polled I/O Path of Linux Proceedings Article In: Proceedings of the 16th ACM Workshop on Hot Topics in Storage and File Systems, pp. 31–37, Association for Computing Machinery, Santa Clara, CA, USA, 2024, ISBN: 9798400706301. |
KDTree-SOM: Self-organizing Map based Anomaly Detection for Lightweight Autonomous Embedded Systems Proceedings Article In: Proceedings of the Great Lakes Symposium on VLSI 2024, pp. 700–705, Association for Computing Machinery, Clearwater, FL, USA, 2024, ISBN: 9798400706059. |
SEAL: Sensing Efficient Active Learning on Wearables through Context-awareness Proceedings Article In: 2024 Design, Automation & Test in Europe Conference & Exhibition (DATE), pp. 1-2, 2024. |
Back to the Future: Reversible Runtime Neural Network Pruning for Safe Autonomous Systems Proceedings Article In: 2024 Design, Automation & Test in Europe Conference & Exhibition (DATE), pp. 1-6, 2024, ISBN: 978-3-9819263-8-5. |
BoostIID: Fault-agnostic Online Detection of WCET Changes in Autonomous Driving Proceedings Article In: 2024 29th Asia and South Pacific Design Automation Conference (ASP-DAC), pp. 704-709, 2024. |
2023 |
A Deep Learning-Based PPG Quality Assessment Approach for Heart Rate and Heart Rate Variability Journal Article In: ACM Trans. Comput. Healthcare, 2023, ISSN: 2691-1957, (Just Accepted). |
EASYR: Energy-Efficient Adaptive System Reconfiguration for Dynamic Deadlines in Autonomous Driving on Multicore Processors Journal Article In: ACM Trans. Embed. Comput. Syst., vol. 22, no. 3, 2023, ISSN: 1539-9087. |
DynaFuse: Dynamic Fusion for Resource Efficient Multi-Modal Machine Learning Inference Conference 2023, ISSN: 1943-0671. |
LOCoCAT: Low-Overhead Classification of CAN Bus Attack Types Conference 2023. |
SAFER: Safety Assurances For Emergent Behavior Journal Article In: IEEE Design & Test, pp. 1-1, 2023. |
Is Garbage Collection Overhead Gone? Case Study of F2FS on ZNS SSDs Proceedings Article In: Proceedings of the 15th ACM Workshop on Hot Topics in Storage and File Systems, pp. 102–108, Association for Computing Machinery, Boston, MA, USA, 2023, ISBN: 9798400702242. |
Locate: Low-Power Viterbi Decoder Exploration Using Approximate Adders Proceedings Article In: Proceedings of the Great Lakes Symposium on VLSI 2023, pp. 409–413, Association for Computing Machinery, Knoxville, TN, USA, 2023, ISBN: 9798400701252. |
Self-awareness in Cyber-Physical Systems: Recent Developments and Open Challenges Proceedings Article In: 2023 Design, Automation & Test in Europe Conference & Exhibition (DATE), pp. 1-6, 2023. |
Loneliness Forecasting Using Multi-modal Wearable and Mobile Sensing in Everyday Settings Journal Article In: medRxiv, 2023. |
Information Processing Factory 2.0 - Self-awareness for Autonomous Collaborative Systems Proceedings Article In: 2023 Design, Automation & Test in Europe Conference & Exhibition (DATE), pp. 1-6, 2023. |
2022 |
Demand Layering for Real-Time DNN Inference with Minimized Memory Usage Proceedings Article In: 2022 IEEE Real-Time Systems Symposium (RTSS), pp. 291-304, 2022. |
ProSwap: Period-aware Proactive Swapping to Maximize Embedded Application Performance Proceedings Article In: 2022 IEEE International Conference on Networking, Architecture and Storage (NAS), pp. 1-4, 2022. |
Exploring computation offloading in IoT systems Journal Article In: Information Systems, vol. 107, 2022, ISSN: 0306-4379. |
Hybrid Learning for Orchestrating Deep Learning Inference in Multi-user Edge-cloud Networks (Best Paper Award) Proceedings Article In: 2022 23rd International Symposium on Quality Electronic Design (ISQED), pp. 1-6, 2022. |
Flexible and Personalized Learning for Wearable Health Applications using HyperDimensional Computing Proceedings Proceedings of the Great Lakes Symposium on VLSI , 2022, ISBN: 9781450393225. |
AMSER: Adaptive Multimodal Sensing for Energy Efficient and Resilient eHealth Systems Conference 2022 Design, Automation Test in Europe Conference Exhibition (DATE), 2022, ISBN: 978-3-9819263-6-1. |
Composing Graphical Models with Generative Adversarial Networks for EEG Signal Modeling Conference Composing Graphical Models with Generative Adversarial Networks for EEG Signal Modeling, 2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), 2022, ISSN: 2379-190X. |
Online Learning for Orchestration of Inference in Multi-User End-Edge-Cloud Networks Journal Article In: ACM Transactions on Embedded Computing Systems (TECS), 2022, ISSN: 1539-9087. |
2021 |
pyEDA: An Open-Source Python Toolkit for Pre-processing and Feature Extraction of Electrodermal Activity Proceedings Article In: The 12th International Conference on Ambient Systems, Networks and Technologies (ANT 2021) / The 4th International Conference on Emerging Data and Industry 4.0 (EDI40 2021) / Affiliated Workshops, March 23-26, 2021, Warsaw, Poland, pp. 99–106, Elsevier, 2021. |
P2E-WGAN: ECG waveform synthesis from PPG with conditional wasserstein generative adversarial networks Proceedings Article In: SAC '21: The 36th ACM/SIGAPP Symposium on Applied Computing, Virtual Event, Republic of Korea, March 22-26, 2021, pp. 1030–1036, ACM, 2021. |
Reflecting on Self-Aware Systems-on-Chip Proceedings Article In: A Journey of Embedded and Cyber-Physical Systems - Essays Dedicated to Peter Marwedel on the Occasion of His 70th Birthday, pp. 79–95, Springer, 2021. |
Data Collection and Labeling of Real-Time IoT-Enabled Bio-Signals in Everyday Settings for Mental Health Improvement Proceedings Article In: Gaggi, Ombretta; Manzoni, Pietro; Palazzi, Claudio E (Ed.): GoodIT '21: Conference on Information Technology for Social Good, Roma, Italy, September 9-11, 2021, pp. 186–191, ACM, 2021. |
Energy-Efficient Adaptive System Reconfiguration for Dynamic Deadlines in Autonomous Driving Proceedings Article In: 24th IEEE International Symposium on Real-Time Distributed Computing, ISORC 2021, Daegu, South Korea, June 1-3, 2021, pp. 96–104, IEEE, 2021. |
Endurance-Aware Mapping of Spiking Neural Networks to Neuromorphic Hardware Journal Article In: CoRR, vol. abs/2103.05707, 2021. |
NeuroXplorer 1.0: An Extensible Framework for Architectural Exploration with Spiking Neural Networks Journal Article In: CoRR, vol. abs/2105.01795, 2021. |
Dynamic Reliability Management in Neuromorphic Computing Journal Article In: CoRR, vol. abs/2105.02038, 2021. |
MARS: Middleware for Adaptive Reflective Computer Systems Journal Article In: CoRR, vol. abs/2107.11417, 2021. |
Personalized Stress Monitoring using Wearable Sensors in Everyday Settings Journal Article In: CoRR, vol. abs/2108.00144, 2021. |
SEAMS: Self-Optimizing Runtime Manager for Approximate Memory Hierarchies Journal Article In: ACM Trans. Embed. Comput. Syst., vol. 20, no. 5, pp. 48:1–48:26, 2021. |
Long-Term IoT-Based Maternal Monitoring: System Design and Evaluation Journal Article In: Sensors, vol. 21, no. 7, pp. 2281, 2021. |
Towards Smart and Efficient Health Monitoring Using Edge-enabled Situational-awareness Proceedings Article In: 3rd IEEE International Conference on Artificial Intelligence Circuits and Systems, AICAS 2021, Washington, DC, USA, June 6-9, 2021, pp. 1–4, IEEE, 2021. |
2020 |
Edge-Assisted Control for Healthcare Internet-of-Things: A Case Study on PPG-based Early Warning Score Journal Article In: ACM transaction on Internet of Things , 2020. |
CryptoPIM: In-memory Acceleration for Lattice-based Cryptographic Hardware Journal Article In: Design Automation Conference (DAC), 2020, (Best paper award finalist). |
Optimizing Energy Efficiency of Wearable Sensors Using Fog-assisted Control Book Chapter In: Fog Computing: Theory and Practice, Chapter 9, pp. arXiv:1907.11989, Wiley, 2020, ISBN: 9781119551713. |
STINT: selective transmission for low-energy physiological monitoring Proceedings Article In: ISLPED '20: ACM/IEEE International Symposium on Low Power Electronics and Design, Boston, Massachusetts, August 10-12, 2020, pp. 115–120, ACM, 2020. |
An Efficient and Robust Deep Learning Method with 1-D Octave Convolution to Extract Fetal Electrocardiogram Journal Article In: Sensors, vol. 20, no. 13, pp. 3757, 2020. |
Data Reuse for Accelerated Approximate Warps Journal Article In: IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems (TCAD), 2020. |
Exploring Energy Efficient Quantum-resistant Signal Processing Using Array Processors Conference International Conference on Acoustics, Speech, and Signal Processing (ICASSP), 2020, (Best paper award finalist). |
Synthesis of Flexible Accelerators for Early Adoption of Ring-LWE Post-quantum Cryptography Journal Article In: Transactions on Embedded Computing Systems, 2020. |
Introduction to the Special Issue on Self-Aware Cyber-physical Systems Journal Article In: ACM Trans. Cyber Phys. Syst., vol. 4, no. 4, pp. 37:1–37:2, 2020. |
Self-aware Cyber-Physical Systems Journal Article In: ACM Trans. Cyber Phys. Syst., vol. 4, no. 4, pp. 38:1–38:26, 2020. |
Mapping Spiking Neural Networks to Neuromorphic Hardware Journal Article In: IEEE Trans. Very Large Scale Integr. Syst., vol. 28, no. 1, pp. 76–86, 2020. |
HCI and mHealth Wearable Tech: A Multidisciplinary Research Challenge Proceedings Article In: Extended Abstracts of the 2020 CHI Conference on Human Factors in Computing Systems, CHI 2020, Honolulu, HI, USA, April 25-30, 2020, pp. 1–7, ACM, 2020. |
Emergent Control of MPSoC Operation by a Hierarchical Supervisor / Reinforcement Learning Approach Proceedings Article In: 2020 Design, Automation & Test in Europe Conference & Exhibition, DATE 2020, Grenoble, France, March 9-13, 2020, pp. 1562–1567, IEEE, 2020. |