Kiwan Maeng

According to our database, Kiwan Maeng authored at least 29 papers between 2017 and 2024.

Collaborative distances:

• Dijkstra number of four.
• Erdős number of three.

2024

Information Flow Control in Machine Learning through Modular Model Architecture.

Trishita Tiwari, Suchin Gururangan, Chuan Guo, Weizhe Hua, Sanjay Kariyappa, Udit Gupta, Wenjie Xiong, Kiwan Maeng, Hsien-Hsin S. Lee, G. Edward Suh

Proceedings of the 33rd USENIX Security Symposium, 2024

Accelerating ReLU for MPC-Based Private Inference with a Communication-Efficient Sign Estimation.

Kiwan Maeng, G. Edward Suh

Proceedings of the Seventh Annual Conference on Machine Learning and Systems, 2024

Compiler-Based Memory Encryption for Machine Learning on Commodity Low-Power Devices.

Kiwan Maeng, Brandon Lucia

Proceedings of the 33rd ACM SIGPLAN International Conference on Compiler Construction, 2024

LazyDP: Co-Designing Algorithm-Software for Scalable Training of Differentially Private Recommendation Models.

Juntaek Lim, Youngeun Kwon, Ranggi Hwang, Kiwan Maeng, G. Edward Suh, Minsoo Rhu

Proceedings of the 29th ACM International Conference on Architectural Support for Programming Languages and Operating Systems, 2024

GPU-based Private Information Retrieval for On-Device Machine Learning Inference.

Maximilian Lam, Jeff Johnson, Wenjie Xiong, Kiwan Maeng, Udit Gupta, Yang Li, Liangzhen Lai, Ilias Leontiadis, Minsoo Rhu, Hsien-Hsin S. Lee, Vijay Janapa Reddi, Gu-Yeon Wei, David Brooks, G. Edward Suh

Proceedings of the 29th ACM International Conference on Architectural Support for Programming Languages and Operating Systems, 2024

2023

Federated Ensemble Learning: Increasing the Capacity of Label Private Recommendation Systems.

Meisam Hejazinia, Dzmitry Huba, Ilias Leontiadis, Kiwan Maeng, Mani Malek, Luca Melis, Ilya Mironov, Milad Nasr, Kaikai Wang, Carole-Jean Wu

IEEE Data Eng. Bull., 2023

Approximating ReLU on a Reduced Ring for Efficient MPC-based Private Inference.

Kiwan Maeng, G. Edward Suh

CoRR, 2023

Green Federated Learning.

Ashkan Yousefpour, Shen Guo, Ashish Shenoy, Sayan Ghosh, Pierre Stock, Kiwan Maeng, Schalk-Willem Krüger, Michael G. Rabbat, Carole-Jean Wu, Ilya Mironov

CoRR, 2023

Bounding the Invertibility of Privacy-preserving Instance Encoding using Fisher Information.

Kiwan Maeng, Chuan Guo, Sanjay Kariyappa, G. Edward Suh

Proceedings of the Advances in Neural Information Processing Systems 36: Annual Conference on Neural Information Processing Systems 2023, 2023

Optimizing CPU Performance for Recommendation Systems At-Scale.

Rishabh Jain, Scott Cheng, Vishwas Kalagi, Vrushabh Sanghavi, Samvit Kaul, Meena Arunachalam, Kiwan Maeng, Adwait Jog, Anand Sivasubramaniam, Mahmut Taylan Kandemir, Chita R. Das

Proceedings of the 50th Annual International Symposium on Computer Architecture, 2023

Cocktail Party Attack: Breaking Aggregation-Based Privacy in Federated Learning Using Independent Component Analysis.

Sanjay Kariyappa, Chuan Guo, Kiwan Maeng, Wenjie Xiong, G. Edward Suh, Moinuddin K. Qureshi, Hsien-Hsin S. Lee

Proceedings of the International Conference on Machine Learning, 2023

Carbon Explorer: A Holistic Framework for Designing Carbon Aware Datacenters.

Bilge Acun, Benjamin Lee, Fiodar Kazhamiaka, Kiwan Maeng, Udit Gupta, Manoj Chakkaravarthy, David Brooks, Carole-Jean Wu

Proceedings of the 28th ACM International Conference on Architectural Support for Programming Languages and Operating Systems, 2023

2022

Data Leakage via Access Patterns of Sparse Features in Deep Learning-based Recommendation Systems.

Hanieh Hashemi, Wenjie Xiong, Liu Ke, Kiwan Maeng, Murali Annavaram, G. Edward Suh, Hsien-Hsin S. Lee

CoRR, 2022

Measuring and Controlling Split Layer Privacy Leakage Using Fisher Information.

Kiwan Maeng, Chuan Guo, Sanjay Kariyappa, G. Edward Suh

CoRR, 2022

FEL: High Capacity Learning for Recommendation and Ranking via Federated Ensemble Learning.

Meisam Hejazinia, Dzmitry Huba, Ilias Leontiadis, Kiwan Maeng, Mani Malek, Luca Melis, Ilya Mironov, Milad Nasr, Kaikai Wang, Carole-Jean Wu

CoRR, 2022

A Holistic Approach for Designing Carbon Aware Datacenters.

Bilge Acun, Benjamin Lee, Kiwan Maeng, Manoj Chakkaravarthy, Udit Gupta, David Brooks, Carole-Jean Wu

CoRR, 2022

Towards Fair Federated Recommendation Learning: Characterizing the Inter-Dependence of System and Data Heterogeneity.

Kiwan Maeng, Haiyu Lu, Luca Melis, John Nguyen, Mike Rabbat, Carole-Jean Wu

Proceedings of the RecSys '22: Sixteenth ACM Conference on Recommender Systems, Seattle, WA, USA, September 18, 2022

Sustainable AI: Environmental Implications, Challenges and Opportunities.

Carole-Jean Wu, Ramya Raghavendra, Udit Gupta, Bilge Acun, Newsha Ardalani, Kiwan Maeng, Gloria Chang, Fiona Aga Behram, Jinshi Huang, Charles Bai, Michael Gschwind, Anurag Gupta, Myle Ott, Anastasia Melnikov, Salvatore Candido, David Brooks, Geeta Chauhan, Benjamin Lee, Hsien-Hsin S. Lee, Bugra Akyildiz, Maximilian Balandat, Joe Spisak, Ravi Jain, Mike Rabbat, Kim M. Hazelwood

Proceedings of the Fifth Conference on Machine Learning and Systems, 2022

An Architectural Charge Management Interface for Energy-Harvesting Systems.

Emily Ruppel, Milijana Surbatovich, Harsh Desai, Kiwan Maeng, Brandon Lucia

Proceedings of the 55th IEEE/ACM International Symposium on Microarchitecture, 2022

2021

Sustainable AI: Environmental Implications, Challenges and Opportunities.

Carole-Jean Wu, Ramya Raghavendra, Udit Gupta, Bilge Acun, Newsha Ardalani, Kiwan Maeng, Gloria Chang, Fiona Aga Behram, James Huang, Charles Bai, Michael Gschwind, Anurag Gupta, Myle Ott, Anastasia Melnikov, Salvatore Candido, David Brooks, Geeta Chauhan, Benjamin Lee, Hsien-Hsin S. Lee, Bugra Akyildiz, Maximilian Balandat, Joe Spisak, Ravi Jain, Mike Rabbat, Kim M. Hazelwood

CoRR, 2021

Understanding and Improving Failure Tolerant Training for Deep Learning Recommendation with Partial Recovery.

Kiwan Maeng, Shivam Bharuka, Isabel Gao, Mark C. Jeffrey, Vikram Saraph, Bor-Yiing Su, Caroline Trippel, Jiyan Yang, Mike Rabbat, Brandon Lucia, Carole-Jean Wu

Proceedings of the Fourth Conference on Machine Learning and Systems, 2021

2020

Dynamic Task-based Intermittent Execution for Energy-harvesting Devices.

Amjad Yousef Majid, Carlo Delle Donne, Kiwan Maeng, Alexei Colin, Kasim Sinan Yildirim, Brandon Lucia, Przemyslaw Pawelczak

ACM Trans. Sens. Networks, 2020

CPR: Understanding and Improving Failure Tolerant Training for Deep Learning Recommendation with Partial Recovery.

Kiwan Maeng, Shivam Bharuka, Isabel Gao, Mark C. Jeffrey, Vikram Saraph, Bor-Yiing Su, Caroline Trippel, Jiyan Yang, Mike Rabbat, Brandon Lucia, Carole-Jean Wu

CoRR, 2020

Adaptive low-overhead scheduling for periodic and reactive intermittent execution.

Kiwan Maeng, Brandon Lucia

Proceedings of the 41st ACM SIGPLAN International Conference on Programming Language Design and Implementation, 2020

2019

Enhancing Stratospheric Weather Analyses and Forecasts by Deploying Sensors from a Weather Balloon.

Kiwan Maeng, Iskender Kushan, Brandon Lucia, Ashish Kapoor

CoRR, 2019

Supporting peripherals in intermittent systems with just-in-time checkpoints.

Kiwan Maeng, Brandon Lucia

Proceedings of the 40th ACM SIGPLAN Conference on Programming Language Design and Implementation, 2019

2018

Adaptive Dynamic Checkpointing for Safe Efficient Intermittent Computing.

Kiwan Maeng, Brandon Lucia

Proceedings of the 13th USENIX Symposium on Operating Systems Design and Implementation, 2018

2017

Alpaca: intermittent execution without checkpoints.

Kiwan Maeng, Alexei Colin, Brandon Lucia

Proc. ACM Program. Lang., 2017

Intermittent Computing: Challenges and Opportunities.

Brandon Lucia, Vignesh Balaji, Alexei Colin, Kiwan Maeng, Emily Ruppel

Proceedings of the 2nd Summit on Advances in Programming Languages, 2017