Conference Proceedings
- Kiwan Maeng and G. Edward Suh, 2024, "Accelerating ReLU for MPC-Based Private Inference with a Communication-Efficient Sign Estimation", MLSys
- Juntaek Lim, Younguen Kwon, Ranggi Hwang, Kiwan Maeng, G. Edward Suh and Minsoo Rhu, 2024, "LazyDP: Co-Designing Algorithm-Software for Scalable Training of Differentially Private Recommendation Models", International Conference on Architectural Support for Programming Languages and Operating Systems (ASPLOS)
- Maximilian Lam, Jeff Johnson, Wenjie Xiong, Kiwan Maeng, Udit Gupta, Minsoo Rhu, Hsien-Hsin S Lee, Vijay Janapa Reddi, Gu-Yeon Wei, David Brooks and G. Edward Suh, 2024, "GPU-based Private Information Retrieval for On-Device Machine Learning Inference", International Conference on Architectural Support for Programming Languages and Operating Systems (ASPLOS)
- Kiwan Maeng and Brandon Lucia, 2024, "Compiler-based Memory Encryption for Machine Learning on Commodity Low-power Devices", International Conference on Compiler Construction (CC)
- Kiwan Maeng, Chuan Guo, Sanjay Kariyappa and G. Edward Suh, 2023, "Bounding the Invertibility of Privacy-preserving Instance Encoding using Fisher Information", Conference on Neural Information Processing Systems (NeurIPS)
- Sanjay Kariyappa, Chuan Guo, Kiwan Maeng, Wenjie Xiong, G Edward Suh, Moinuddin K Qureshi and Hsien-Hsin S Lee, 2023, "Cocktail Party Attack: Breaking Aggregation-Based Privacy in Federated Learning using Independent Component Analysis", International Conference on Machine Learning (ICML)
- Rishabh Jain, Scott Cheng, Vishwas Kalagi, Vrushabh Sanghavi, Samvit Kaul, Meena Arunachalam, Kiwan Maeng, Adwait Jog, Anand Sivasubramaniam, Mahmut T Kandemir and Chitaranjan Das, 2023, "Optimizing CPU Performance for Recommendation Systems At-Scale", Proceedings of the 46th International Symposium on Computer Architecture (ISCA)
- Bilge Acun, Benjamin Lee, Fiodar Kazhamiaka, Kiwan Maeng, Udit Gupta, Manoj Chakkaravarthy, David Brooks and Carole-Jean Wu, 2023, "Carbon Explorer: A Holistic Framework for Designing Carbon Aware Datacenters", Proceedings of the 28th ACM International Conference on Architectural Support for Programming Languages and Operating Systems (ASPLOS), pp. 118--132
- Kiwan Maeng, Haiyu Lu, Luca Melis, John Nguyen, Mike Rabbat and Carole-Jean Wu, 2022, "Towards fair federated recommendation learning: Characterizing the inter-dependence of system and data heterogeneity", Proceedings of the 16th ACM Conference on Recommender Systems (RecSys), pp. 156--167
- Emily Ruppel, Milijana Surbatovich, Harsh Desai, Kiwan Maeng and Brandon Lucia, 2022, "An Architectural Charge Management Interface for Energy-Harvesting Systems", 2022 55th IEEE/ACM International Symposium on Microarchitecture (MICRO), pp. 318--335
- Carole-Jean Wu, Ramya Raghavendra, Udit Gupta, Bilge Acun, Newsha Ardalani, Kiwan Maeng, Gloria Chang, Fiona Aga, 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, Max Balandat, Joe Spisak, Ravi Jain, Mike Rabbat and Kim Hazelwood, 2022, "Sustainable ai: Environmental implications, challenges and opportunities", Proceedings of Machine Learning and Systems (MLSys), 4, pp. 795--813
Manuscripts
- Hanieh Hashemi, Wenjie Xiong, Liu Ke, Kiwan Maeng, Murali Annavaram, G. Edward Suh and Hsien-Hsin S Lee, 2022, "Data Leakage via Access Patterns of Sparse Features in Deep Learning-based Recommendation Systems", arXiv preprint arXiv:2212.06264
- Kiwan Maeng, Chuan Guo, Sanjay Kariyappa and Edward Suh, 2022, "Measuring and Controlling Split Layer Privacy Leakage Using Fisher Information", NeurIPS 2022 Federated Learning Workshop
