Write Invalidation Analysis in Chip Multiprocessors. - Newsha Ardalani; Amirali Baniasadi (PATMOS, 2009)

Cross-architecture performance prediction (XAPP) using CPU code to predict GPU performance. - Newsha Ardalani; Clint Lestourgeon; Karthikeyan Sankaralingam; Xiaojin Zhu 001 (MICRO, 2015)

Stream-Dataflow Acceleration. - Tony Nowatzki; Vinay Gangadhar; Newsha Ardalani; Karthikeyan Sankaralingam (ISCA, 2017)

Deep Learning Scaling is Predictable, Empirically. - Joel Hestness; Sharan Narang; Newsha Ardalani; Gregory F. Diamos; Heewoo Jun; Hassan Kianinejad; Md. Mostofa Ali Patwary; Yang Yang; Yanqi Zhou (CoRR, 2017)

Hybrid optimization/heuristic instruction scheduling for programmable accelerator codesign. - Tony Nowatzki; Newsha Ardalani; Karthikeyan Sankaralingam; Jian Weng 002 (PACT, 2018)

Beyond human-level accuracy: computational challenges in deep learning. - Joel Hestness; Newsha Ardalani; Gregory F. Diamos (PPoPP, 2019)

A Static Analysis-based Cross-Architecture Performance Prediction Using Machine Learning. - Newsha Ardalani; Urmish Thakker; Aws Albarghouthi; Karu Sankaralingam (CoRR, 2019)

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; et al. (CoRR, 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; Jinshi Huang; Charles Bai; Michael Gschwind; Anurag Gupta; et al. (MLSys, 2022)

Time and the Value of Data. - Ehsan Valavi; Joel Hestness; Newsha Ardalani; Marco Iansiti (CoRR, 2022)

Time Dependency, Data Flow, and Competitive Advantage. - Ehsan Valavi; Joel Hestness; Marco Iansiti; Newsha Ardalani; Feng Zhu 023; Karim R. Lakhani (CoRR, 2022)

DataPerf: Benchmarks for Data-Centric AI Development. - Mark Mazumder; Colby R. Banbury; Xiaozhe Yao; Bojan Karlas; William Gaviria Rojas; Sudnya Frederick Diamos; Greg Diamos; Lynn He; Douwe Kiela; David Jurado; David Kanter; Rafael Mosquera; et al. (CoRR, 2022)

Understanding Scaling Laws for Recommendation Models. - Newsha Ardalani; Carole-Jean Wu; Zeliang Chen; Bhargav Bhushanam; Adnan Aziz (CoRR, 2022)

MP-Rec: Hardware-Software Co-design to Enable Multi-path Recommendation. - Samuel Hsia; Udit Gupta; Bilge Acun; Newsha Ardalani; Pan Zhong; Gu-Yeon Wei; David Brooks 001; Carole-Jean Wu (ASPLOS (3), 2023)

DataPerf: Benchmarks for Data-Centric AI Development. - Mark Mazumder; Colby R. Banbury; Xiaozhe Yao; Bojan Karlas; William Gaviria Rojas; Sudnya Frederick Diamos; Greg Diamos; Lynn He; Alicia Parrish; Hannah Rose Kirk; Jessica Quaye; Charvi Rastogi; et al. (NeurIPS, 2023)

Towards MoE Deployment: Mitigating Inefficiencies in Mixture-of-Expert (MoE) Inference. - Haiyang Huang 003; Newsha Ardalani; Anna Y. Sun; Liu Ke 001; Hsien-Hsin S. Lee; Anjali Sridhar; Shruti Bhosale; Carole-Jean Wu; Benjamin Lee (CoRR, 2023)

Data Acquisition: A New Frontier in Data-centric AI. - Lingjiao Chen; Bilge Acun; Newsha Ardalani; Yifan Sun 010; Feiyang Kang; Hanrui Lyu; Yongchan Kwon; Ruoxi Jia 001; Carole-Jean Wu; Matei Zaharia; James Zou 001 (CoRR, 2023)

Decoding Data Quality via Synthetic Corruptions: Embedding-guided Pruning of Code Data. - Yu Yang; Aaditya K. Singh; Mostafa Elhoushi; Anas Mahmoud 002; Kushal Tirumala; Fabian Gloeckle; Baptiste Rozière; Carole-Jean Wu; Ari S. Morcos; Newsha Ardalani (CoRR, 2023)

Sieve: Multimodal Dataset Pruning Using Image Captioning Models. - Anas Mahmoud 002; Mostafa Elhoushi; Amro Abbas; Yu Yang; Newsha Ardalani; Hugh Leather; Ari S. Morcos (CVPR, 2024)

MAD-Max Beyond Single-Node: Enabling Large Machine Learning Model Acceleration on Distributed Systems. - Samuel Hsia; Alicia Golden; Bilge Acun; Newsha Ardalani; Zachary DeVito; Gu-Yeon Wei; David Brooks 001; Carole-Jean Wu (ISCA, 2024)

Toward Efficient Inference for Mixture of Experts. - Haiyang Huang 003; Newsha Ardalani; Anna Y. Sun; Liu Ke 001; Shruti Bhosale; Hsien-Hsin S. Lee; Carole-Jean Wu; Benjamin Lee (NeurIPS, 2024)

Text Quality-Based Pruning for Efficient Training of Language Models. - Vasu Sharma; Karthik Padthe; Newsha Ardalani; Kushal Tirumala; Russell Howes; Hu Xu 001; Po-Yao Huang 001; Shang-Wen Li 001; Armen Aghajanyan; Gargi Ghosh; Luke Zettlemoyer (CoRR, 2024)

DeepFlow: A Cross-Stack Pathfinding Framework for Distributed AI Systems. - Newsha Ardalani; Saptadeep Pal; Puneet Gupta 001 (ACM Trans. Design Autom. Electr. Syst., 2024)

Carbon Aware Transformers Through Joint Model-Hardware Optimization. - Irene Wang; Newsha Ardalani; Mostafa Elhoushi; Daniel Jiang; Samuel Hsia; Ekin Sumbul; Divya Mahajan 001; Carole-Jean Wu; Bilge Acun (CoRR, 2025)

Composer: A Search Framework for Hybrid Neural Architecture Design. - Bilge Acun; Prasoon Sinha; Newsha Ardalani; Sangmin Bae; Alicia Golden; Chien-Yu Lin; Meghana Madhyastha; Fei Sun; Neeraja J. Yadwadkar; Carole-Jean Wu (CoRR, 2025)

Quagmires in SFT-RL Post-Training: When High SFT Scores Mislead and What to Use Instead. - Feiyang Kang; Michael Kuchnik; Karthik Padthe; Marin Vlastelica; Ruoxi Jia 001; Carole-Jean Wu; Newsha Ardalani (CoRR, 2025)

Demystifying Synthetic Data in LLM Pre-training: A Systematic Study of Scaling Laws, Benefits, and Pitfalls. - Feiyang Kang; Newsha Ardalani; Michael Kuchnik; Youssef Emad; Mostafa Elhoushi; Shubhabrata Sengupta; Shang-wen Li 001; Ramya Raghavendra; Ruoxi Jia 001; Carole-Jean Wu (CoRR, 2025)