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Swarnava Dey
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2020 – today
- 2024
- [c41]Swarnava Dey
, Arijit Mukherjee
Towards a Task-agnostic Distillation Methodology for Creating Edge Foundation Models. EdgeFM@MobiSys 2024: 10-15 - [c40]Shalini Mukhopadhyay
Demo: Stress Detection on Tiny Edge Device with GSR Sensor. MobiSys 2024: 598-599 - [c39]Swarnava Dey:
Keynote: AutoML for TinyML. PerCom Workshops 2024: 159 - [c38]Soma Dasgupta, Swarnava Dey:
MCU-based TinyML Camera for Face Recognition. PerCom Workshops 2024: 391-393 - [c37]Shalini Mukhopadhyay, Swarnava Dey, Arijit Mukherjee, Arpan Pal, Ashwin S:
Time Series Classification on Edge with Lightweight Attention Networks. PerCom Workshops 2024: 487-492 - [c36]Swarnava Dey, Soma Dasgupta:
A Genetic Tuner for Fixed-skeleton TinyML Models. PerCom Workshops 2024: 556-561 - 2023
- [c35]Jayeeta Mondal
Binary Convolutional Neural Network for Efficient Gesture Recognition at Edge. AIMLSystems 2023: 18:1-18:10 - [c34]Swarnava Dey
Designing a Bare Minimum Face Recognition Architecture for Bare Metal Edge Devices: An Experience Report. AIMLSystems 2023: 19:1-19:8 - [c33]Syed Mujibul Islam
TinyML Demonstration of Time-series Prediction and Vision-based Gesture Recognition. AIMLSystems 2023: 60:1-60:3 - [c32]Swarnava Dey
DietCNN: Multiplication-free Inference for Quantized CNNs. IJCNN 2023: 1-8 - [c31]Swarnava Dey, Avik Ghose, Soumik Das:
Challenges of Accurate and Efficient AutoML. ASE 2023: 1834-1839 - [c30]Shalini Mukhopadhyay
TinyPuff: Automated design of Tiny Smoking Puff Classifiers for Body Worn Devices. BodySys@MobiSys 2023: 7-12 - [c29]Shalini Mukhopadhyay
Demo: On-device Puff Detection System for Smoking Cessation. MobiSys 2023: 586-587 - [c28]Shalini Mukhopadhyay, Swarnava Dey
Generating Tiny Deep Neural Networks for ECG Classification on Micro-Controllers. PerCom Workshops 2023: 392-397 - [c27]Varsha Sharma
PuffConv: A System for Online and On-device Puff Detection for Smoking Cessation. PerCom Workshops 2023: 595-600 - [c26]Shalini Mukhopadhyay
Demo Abstract: Lightweight Attention Network for Time Series Classification on Edge. SenSys 2023: 484-485 - [i2]Swarnava Dey, Pallab Dasgupta, Partha Pratim Chakrabarti:
DietCNN: Multiplication-free Inference for Quantized CNNs. CoRR abs/2305.05274 (2023) - [i1]Jayeeta Mondal, Swarnava Dey, Arijit Mukherjee:
Automated Heterogeneous Low-Bit Quantization of Multi-Model Deep Learning Inference Pipeline. CoRR abs/2311.05870 (2023) - 2022
- [j1]Arijit Mukherjee
Accelerated Fire Detection and Localization at Edge. ACM Trans. Embed. Comput. Syst. 21(6): 70:1-70:27 (2022) - [c25]Jeet Dutta
Acceleration-aware, Retraining-free Evolutionary Pruning for Automated Fitment of Deep Learning Models on Edge Devices. AIMLSystems 2022: 10:1-10:10 - [c24]Arijit Mukherjee
Automated Deep Learning Model Partitioning for Heterogeneous Edge Devices. AIMLSystems 2022: 19:1-19:8 - [c23]Arijit Mukherjee
TinyML Techniques for running Machine Learning models on Edge Devices. AIMLSystems 2022: 27:1-27:2 - [c22]Swarnava Dey
SymDNN: Simple & Effective Adversarial Robustness for Embedded Systems. CVPR Workshops 2022: 3598-3608 - [c21]Shalini Mukhopadhyay, Swarnava Dey, Avik Ghose, Aakash Tyagi:
Automated Generation of Tiny Model for Real-Time ECG Classification on Tiny Edge Devices. SenSys 2022: 756-757 - 2020
- [c20]Swarnava Dey, Jeet Dutta:
A Low footprint Automatic Speech Recognition System For Resource Constrained Edge Devices. AIChallengeIoT@SenSys 2020: 48-54
2010 – 2019
- 2019
- [c19]Jayeeta Mondal, Swarnava Dey
Edge Acceleration of Deep Neural Networks. MobiSys 2019: 691-692 - [c18]Swarnava Dey
Offloaded Execution of Deep Learning Inference at Edge: Challenges and Insights. PerCom Workshops 2019: 855-861 - [c17]Swarnava Dey
Embedded Deep Inference in Practice: Case for Model Partitioning. SenSys-ML 2019: 25-30 - 2018
- [c16]Swagata Biswas, Swarnava Dey
(WIP) At Most M - A Flexible Redundancy Model for Cloud Robotics. IEEE CLOUD 2018: 577-581 - [c15]Swarnava Dey
Implementing Deep Learning and Inferencing on Fog and Edge Computing Systems. PerCom Workshops 2018: 818-823 - [c14]Swarnava Dey
Partitioning of CNN Models for Execution on Fog Devices. CitiFog@SenSys 2018: 19-24 - 2017
- [c13]Swagata Biswas, Swarnava Dey, Rimita Lahiri, Arijit Mukherjee:
A Distributed and Fault Tolerant Robotic Localisation and Mapping in Network Edge. ARMS-CC@PODC 2017: 7-16 - 2016
- [c12]Swarnava Dey
CAD Patient Classification Using MIMIC-II. eHealth 360° 2016: 370-375 - [c11]Swarnava Dey
Robotic SLAM: a Review from Fog Computing and Mobile Edge Computing Perspective. MobiQuitous (Adjunct Proceedings) 2016: 153-158 - 2014
- [c10]Arijit Mukherjee
To Run or Not to Run: Predicting Resource Usage Pattern in a Smartphone. IoT360 (1) 2014: 367-375 - [c9]Swarnava Dey, Arijit Mukherjee
Dual scheme phone: a user assisted mechanism to effectively run sensor analytics applications on smartphones. MobiQuitous 2014: 347-349 - [c8]Ansuman Banerjee, Himadri Sekhar Paul, Arijit Mukherjee
A Framework for Speculative Scheduling and Device Selection for Task Execution on a Mobile Cloud. ARMS-CC@PODC 2014: 36-51 - [c7]Swarnava Dey, Pubali Datta, Arijit Mukherjee
Facilitating continued run of sensor data analytics services using user driven proactive memory reclamation scheme. SenSys 2014: 338-339 - [c6]Arijit Mukherjee
ANGELS for distributed analytics in IoT. WF-IoT 2014: 565-570 - 2013
- [c5]Swarnava Dey
Challenges of Using Edge Devices in IoT Computation Grids. ICPADS 2013: 564-569 - [c4]Ansuman Banerjee, Arijit Mukherjee
Offloading work to mobile devices: an availability-aware data partitioning approach. MCS@Middleware 2013: 4:1-4:6 - [c3]Arijit Mukherjee
Utilising condor for data parallel analytics in an IoT context - An experience report. WiMob 2013: 325-331 - 2012
- [c2]Prateep Misra, Nilanjan Roy, Soumitra Naskar, Swarnava Dey
An Erasure Coded Archival Storage System. ICPADS 2012: 720-721 - [c1]Swarnava Dey
Smart city surveillance: Leveraging benefits of cloud data stores. LCN Workshops 2012: 868-876
Coauthor Index
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