Characterization and dynamic mitigation of intra-application cache interference. - Carole-Jean Wu; Margaret Martonosi (ISPASS, 2011)

SHiP: signature-based hit predictor for high performance caching. - Carole-Jean Wu; Aamer Jaleel; William Hasenplaugh; Margaret Martonosi; Simon C. Steely Jr.; Joel S. Emer (MICRO, 2011)

PACMan: prefetch-aware cache management for high performance caching. - Carole-Jean Wu; Aamer Jaleel; Margaret Martonosi; Simon C. Steely Jr.; Joel S. Emer (MICRO, 2011)

Adaptive timekeeping replacement: Fine-grained capacity management for shared CMP caches. - Carole-Jean Wu; Margaret Martonosi (ACM Trans. Archit. Code Optim., 2011)

Performance, energy characterizations and architectural implications of an emerging mobile platform benchmark suite - MobileBench. - Dhinakaran Pandiyan; Shin-Ying Lee; Carole-Jean Wu (IISWC, 2013)

CAWS: criticality-aware warp scheduling for GPGPU workloads. - Shin-Ying Lee; Carole-Jean Wu (PACT, 2014)

Quantitative Analysis of Control Flow Checking Mechanisms for Soft Errors. - Aviral Shrivastava; Abhishek Rhisheekesan; Reiley Jeyapaul; Carole-Jean Wu (DAC, 2014)

ReMAP: Reuse and memory access cost aware eviction policy for last level cache management. - Akhil Arunkumar; Carole-Jean Wu (ICCD, 2014)

Quantifying the energy cost of data movement for emerging smart phone workloads on mobile platforms. - Dhinakaran Pandiyan; Carole-Jean Wu (IISWC, 2014)

Characterizing the latency hiding ability of GPUs. - Shin-Ying Lee; Carole-Jean Wu (ISPASS, 2014)

Architectural Thermal Energy Harvesting Opportunities for Sustainable Computing. - Carole-Jean Wu (IEEE Comput. Archit. Lett., 2014)

STEAM: A Smart Temperature and Energy Aware Multicore Controller. - Vinay Hanumaiah; Digant Desai; Benjamin Gaudette; Carole-Jean Wu; Sarma B. K. Vrudhula (ACM Trans. Embed. Comput. Syst., 2014)

Characterization and Throttling-Based Mitigation of Memory Interference for Heterogeneous Smartphones. - Davesh Shingari; Akhil Arunkumar; Carole-Jean Wu (IISWC, 2015)

CAWA: coordinated warp scheduling and cache prioritization for critical warp acceleration of GPGPU workloads. - Shin-Ying Lee; Akhil Arunkumar; Carole-Jean Wu (ISCA, 2015)

A study of mobile device utilization. - Cao Gao; Anthony Gutierrez; Madhav Rajan; Ronald G. Dreslinski; Trevor N. Mudge; Carole-Jean Wu (ISPASS, 2015)

E-ECC: Low Power Erasure and Error Correction Schemes for Increasing Reliability of Commodity DRAM Systems. - Hsing Min Chen; Akhil Arunkumar; Carole-Jean Wu; Trevor N. Mudge; Chaitali Chakrabarti (MEMSYS, 2015)

Improving smartphone user experience by balancing performance and energy with probabilistic QoS guarantee. - Benjamin Gaudette; Carole-Jean Wu; Sarma B. K. Vrudhula (HPCA, 2016)

Ctrl-C: Instruction-Aware Control Loop Based Adaptive Cache Bypassing for GPUs. - Shin-Ying Lee; Carole-Jean Wu (ICCD, 2016)

ID-cache: instruction and memory divergence based cache management for GPUs. - Akhil Arunkumar; Shin-Ying Lee; Carole-Jean Wu (IISWC, 2016)

RATT-ECC: Rate Adaptive Two-Tiered Error Correction Codes for Reliable 3D Die-Stacked Memory. - Hsing Min Chen; Carole-Jean Wu; Trevor N. Mudge; Chaitali Chakrabarti (ACM Trans. Archit. Code Optim., 2016)

Using Low Cost Erasure and Error Correction Schemes to Improve Reliability of Commodity DRAM Systems. - Hsing Min Chen; Supreet Jeloka; Akhil Arunkumar; David T. Blaauw; Carole-Jean Wu; Trevor N. Mudge; Chaitali Chakrabarti (IEEE Trans. Computers, 2016)

Performance characterization, prediction, and optimization for heterogeneous systems with multi-level memory interference. - Shin-Ying Lee; Carole-Jean Wu (IISWC, 2017)

Understanding the thermal challenges of high-performance mobile devices with a detailed platform temperature model. - Ying-Ju Yu; Carole-Jean Wu (IISWC, 2017)

MCM-GPU: Multi-Chip-Module GPUs for Continued Performance Scalability. - Akhil Arunkumar; Evgeny Bolotin; Benjamin Y. Cho; Ugljesa Milic; Eiman Ebrahimi; Oreste Villa; Aamer Jaleel; Carole-Jean Wu; David W. Nellans (ISCA, 2017)

LATTE-CC: Latency Tolerance Aware Adaptive Cache Compression Management for Energy Efficient GPUs. - Akhil Arunkumar; Shin-Ying Lee; Vignesh Soundararajan; Carole-Jean Wu (HPCA, 2018)

DORA: Optimizing Smartphone Energy Efficiency and Web Browser Performance under Interference. - Davesh Shingari; Akhil Arunkumar; Benjamin Gaudette; Sarma B. K. Vrudhula; Carole-Jean Wu (ISPASS, 2018)

Understanding the Future of Energy Efficiency in Multi-Module GPUs. - Akhil Arunkumar; Evgeny Bolotin; David W. Nellans; Carole-Jean Wu (HPCA, 2019)

Machine Learning at Facebook: Understanding Inference at the Edge. - Carole-Jean Wu; David Brooks 001; Kevin Chen; Douglas Chen; Sy Choudhury; Marat Dukhan; Kim M. Hazelwood; Eldad Isaac; Yangqing Jia; Bill Jia; Tommer Leyvand; Hao Lu; et al. (HPCA, 2019)

Genetic improvement of GPU code. - Jhe-Yu Liou; Stephanie Forrest; Carole-Jean Wu (GI@ICSE, 2019)

Deep Learning Recommendation Model for Personalization and Recommendation Systems. - Maxim Naumov; Dheevatsa Mudigere; Hao-Jun Michael Shi; Jianyu Huang; Narayanan Sundaraman; Jongsoo Park; Xiaodong Wang 020; Udit Gupta; Carole-Jean Wu; Alisson G. Azzolini; Dmytro Dzhulgakov; Andrey Mallevich; et al. (CoRR, 2019)

The Architectural Implications of Facebook's DNN-based Personalized Recommendation. - Udit Gupta; Xiaodong Wang 020; Maxim Naumov; Carole-Jean Wu; Brandon Reagen; David Brooks 001; Bradford Cottel; Kim M. Hazelwood; Bill Jia; Hsien-Hsin S. Lee; Andrey Malevich; Dheevatsa Mudigere; et al. (CoRR, 2019)

MLPerf Training Benchmark. - Peter Mattson; Christine Cheng; Cody Coleman; Greg Diamos; Paulius Micikevicius; David A. Patterson 001; Hanlin Tang; Gu-Yeon Wei; Peter Bailis; Victor Bittorf; David Brooks 001; Dehao Chen; et al. (CoRR, 2019)

Configurable-ECC: Architecting a Flexible ECC Scheme to Support Different Sized Accesses in High Bandwidth Memory Systems. - Hsing Min Chen; Shin-Ying Lee; Trevor N. Mudge; Carole-Jean Wu; Chaitali Chakrabarti (IEEE Trans. Computers, 2019)

Optimizing User Satisfaction of Mobile Workloads Subject to Various Sources of Uncertainties. - Benjamin Gaudette; Carole-Jean Wu; Sarma B. K. Vrudhula (IEEE Trans. Mob. Comput., 2019)

Emerging Neural Workloads and Their Impact on Hardware. - David Brooks 001; Martin M. Frank; Tayfun Gokmen; Udit Gupta; Xiaobo Sharon Hu; Shubham Jain; Ann Franchesca Laguna; Michael T. Niemier; Ian O'Connor; Anand Raghunathan; Ashish Ranjan 001; Dayane Reis; et al. (DATE, 2020)

GEVO-ML: a proposal for optimizing ML code with evolutionary computation. - Jhe-Yu Liou; Xiaodong Wang 020; Stephanie Forrest; Carole-Jean Wu (GECCO Companion, 2020)

The Architectural Implications of Facebook's DNN-Based Personalized Recommendation. - Udit Gupta; Carole-Jean Wu; Xiaodong Wang 020; Maxim Naumov; Brandon Reagen; David Brooks 001; Bradford Cottel; Kim M. Hazelwood; Mark Hempstead; Bill Jia; Hsien-Hsin S. Lee; Andrey Malevich; et al. (HPCA, 2020)

Cross-Stack Workload Characterization of Deep Recommendation Systems. - Samuel Hsia; Udit Gupta; Mark Wilkening; Carole-Jean Wu; Gu-Yeon Wei; David Brooks 001 (IISWC, 2020)

DeepRecSys: A System for Optimizing End-To-End At-Scale Neural Recommendation Inference. - Udit Gupta; Samuel Hsia; Vikram Saraph; Xiaodong Wang 020; Brandon Reagen; Gu-Yeon Wei; Hsien-Hsin S. Lee; David Brooks 001; Carole-Jean Wu (ISCA, 2020)

RecNMP: Accelerating Personalized Recommendation with Near-Memory Processing. - Liu Ke 001; Udit Gupta; Benjamin Youngjae Cho; David Brooks 001; Vikas Chandra; Utku Diril; Amin Firoozshahian; Kim M. Hazelwood; Bill Jia; Hsien-Hsin S. Lee; Meng Li 004; Bert Maher; et al. (ISCA, 2020)

MLPerf Inference Benchmark. - Vijay Janapa Reddi; Christine Cheng; David Kanter; Peter Mattson; Guenther Schmuelling; Carole-Jean Wu; Brian Anderson; Maximilien Breughe; Mark Charlebois; William Chou; Ramesh Chukka; Cody Coleman; et al. (ISCA, 2020)

AutoScale: Energy Efficiency Optimization for Stochastic Edge Inference Using Reinforcement Learning. - Young Geun Kim; Carole-Jean Wu (MICRO, 2020)

MLPerf Training Benchmark. - Peter Mattson; Christine Cheng; Gregory F. Diamos; Cody Coleman; Paulius Micikevicius; David A. Patterson 001; Hanlin Tang; Gu-Yeon Wei; Peter Bailis; Victor Bittorf; David Brooks 001; Dehao Chen; et al. (MLSys, 2020)

Developing a Recommendation Benchmark for MLPerf Training and Inference. - Carole-Jean Wu; Robin Burke; Ed H. Chi; Joseph A. Konstan; Julian J. McAuley; Yves Raimond; Hao Zhang (CoRR, 2020)

AutoScale: Optimizing Energy Efficiency of End-to-End Edge Inference under Stochastic Variance. - Young Geun Kim; Carole-Jean Wu (CoRR, 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)

MLPerf: An Industry Standard Benchmark Suite for Machine Learning Performance. - Peter Mattson; Hanlin Tang; Gu-Yeon Wei; Carole-Jean Wu; Vijay Janapa Reddi; Christine Cheng; Cody Coleman; Greg Diamos; David Kanter; Paulius Micikevicius; David A. Patterson 001; Guenther Schmuelling (IEEE Micro, 2020)

GEVO: GPU Code Optimization Using Evolutionary Computation. - Jhe-Yu Liou; Xiaodong Wang 020; Stephanie Forrest; Carole-Jean Wu (ACM Trans. Archit. Code Optim., 2020)

RecSSD: near data processing for solid state drive based recommendation inference. - Mark Wilkening; Udit Gupta; Samuel Hsia; Caroline Trippel; Carole-Jean Wu; David Brooks 001; Gu-Yeon Wei (ASPLOS, 2021)

Understanding Training Efficiency of Deep Learning Recommendation Models at Scale. - Bilge Acun; Matthew Murphy; Xiaodong Wang 020; Jade Nie; Carole-Jean Wu; Kim M. Hazelwood (HPCA, 2021)

Understanding Capacity-Driven Scale-Out Neural Recommendation Inference. - Michael Lui; Yavuz Yetim; Özgür Özkan; Zhuoran Zhao; Shin-Yeh Tsai; Carole-Jean Wu; Mark Hempstead (ISPASS, 2021)

RecPipe: Co-designing Models and Hardware to Jointly Optimize Recommendation Quality and Performance. - Udit Gupta; Samuel Hsia; Jeff Zhang 001; Mark Wilkening; Javin Pombra; Hsien-Hsin Sean Lee; Gu-Yeon Wei; Carole-Jean Wu; David Brooks 001 (MICRO, 2021)

AutoFL: Enabling Heterogeneity-Aware Energy Efficient Federated Learning. - Young Geun Kim; Carole-Jean Wu (MICRO, 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 (MLSys, 2021)

TT-Rec: Tensor Train Compression for Deep Learning Recommendation Models. - Chunxing Yin; Bilge Acun; Carole-Jean Wu; Xing Liu (MLSys, 2021)

Energy-Efficient Mapping for a Network of DNN Models at the Edge. - Mehdi Ghasemi 003; Soroush Heidari; Young Geun Kim; Aaron Lamb; Carole-Jean Wu; Sarma B. K. Vrudhula (SMARTCOMP, 2021)

SVP-CF: Selection via Proxy for Collaborative Filtering Data. - Noveen Sachdeva; Carole-Jean Wu; Julian J. McAuley (CoRR, 2021)

Socio-Technological Challenges and Opportunities: Paths Forward. - Carole-Jean Wu; Srilatha Manne; Parthasarathy Ranganathan; Sarah Bird; Shane Greenstein (CoRR, 2021)

Understanding and Co-designing the Data Ingestion Pipeline for Industry-Scale RecSys Training. - Mark Zhao; Niket Agarwal; Aarti Basant; Bugra Gedik; Satadru Pan; Mustafa Ozdal; Rakesh Komuravelli; Jerry Pan; Tianshu Bao; Haowei Lu 004; Sundaram Narayanan; Jack Langman; 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; James Huang; Charles Bai; Michael Gschwind; Anurag Gupta; et al. (CoRR, 2021)

SecNDP: Secure Near-Data Processing with Untrusted Memory. - Wenjie Xiong 001; Liu Ke 001; Dimitrije Jankov; Michael Kounavis; Xiaochen Wang; Eric Northup; Jie Amy Yang; Bilge Acun; Carole-Jean Wu; Ping Tak Peter Tang; G. Edward Suh; Xuan Zhang 001; et al. (IACR Cryptol. ePrint Arch., 2021)

Low-Precision Hardware Architectures Meet Recommendation Model Inference at Scale. - Zhaoxia Deng; Jongsoo Park; Ping Tak Peter Tang; Haixin Liu; Jie Yang; Hector Yuen; Jianyu Huang; Daya Shanker Khudia; Xiaohan Wei; Ellie Wen; Dhruv Choudhary; Raghuraman Krishnamoorthi; et al. (IEEE Micro, 2021)

The Vision Behind MLPerf: Understanding AI Inference Performance. - Vijay Janapa Reddi; Christine Cheng; David Kanter; Peter Mattson; Guenther Schmuelling; Carole-Jean Wu (IEEE Micro, 2021)

Dynamic Temperature Management of Near-Sensor Processing for Energy-Efficient High-Fidelity Imaging. - Venkatesh Kodukula; Saad Katrawala; Britton Jones; Carole-Jean Wu; Robert LiKamWa (Sensors, 2021)

Exploiting Parallelism Opportunities with Deep Learning Frameworks. - Yu Emma Wang; Carole-Jean Wu; Xiaodong Wang 020; Kim M. Hazelwood; David Brooks 001 (ACM Trans. Archit. Code Optim., 2021)

RecShard: statistical feature-based memory optimization for industry-scale neural recommendation. - Geet Sethi; Bilge Acun; Niket Agarwal; Christos Kozyrakis; Caroline Trippel; Carole-Jean Wu (ASPLOS, 2022)

A joint management middleware to improve training performance of deep recommendation systems with SSDs. - Chun-Feng Wu; Carole-Jean Wu; Gu-Yeon Wei; David Brooks 001 (DAC, 2022)

Hercules: Heterogeneity-Aware Inference Serving for At-Scale Personalized Recommendation. - Liu Ke 001; Udit Gupta; Mark Hempstead; Carole-Jean Wu; Hsien-Hsin S. Lee; Xuan Zhang 001 (HPCA, 2022)

FedGPO: Heterogeneity-Aware Global Parameter optimization for Efficient Federated Learning. - Young Geun Kim; Carole-Jean Wu (IISWC, 2022)

Understanding the Power of Evolutionary Computation for GPU Code Optimization. - Jhe-Yu Liou; Muaaz Awan; Steven A. Hofmeyr; Stephanie Forrest; Carole-Jean Wu (IISWC, 2022)

ACT: designing sustainable computer systems with an architectural carbon modeling tool. - Udit Gupta; Mariam Elgamal; Gage Hills; Gu-Yeon Wei; Hsien-Hsin S. Lee; David Brooks 001; Carole-Jean Wu (ISCA, 2022)

Understanding data storage and ingestion for large-scale deep recommendation model training: industrial product. - Mark Zhao; Niket Agarwal; Aarti Basant; Bugra Gedik; Satadru Pan; Mustafa Ozdal; Rakesh Komuravelli; Jerry Pan; Tianshu Bao; Haowei Lu 004; Sundaram Narayanan; Jack Langman; et al. (ISCA, 2022)

PAPAYA: Practical, Private, and Scalable Federated Learning. - Dzmitry Huba; John Nguyen; Kshitiz Malik; Ruiyu Zhu; Mike Rabbat; Ashkan Yousefpour; Carole-Jean Wu; Hongyuan Zhan; Pavel Ustinov; Harish Srinivas; Kaikai Wang; Anthony Shoumikhin; et al. (MLSys, 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; et al. (MLSys, 2022)

Infinite Recommendation Networks: A Data-Centric Approach. - Noveen Sachdeva; Mehak Preet Dhaliwal; Carole-Jean Wu; Julian J. McAuley (NeurIPS, 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 (RecSys, 2022)

On Sampling Collaborative Filtering Datasets. - Noveen Sachdeva; Carole-Jean Wu; Julian J. McAuley (WSDM, 2022)

A Holistic Approach for Designing Carbon Aware Datacenters. - Bilge Acun; Benjamin Lee; Kiwan Maeng; Manoj Chakkaravarthy; Udit Gupta; David Brooks 001; Carole-Jean Wu (CoRR, 2022)

FEL: High Capacity Learning for Recommendation and Ranking via Federated Ensemble Learning. - Meisam Hejazinia; Dzmitry Huba; Ilias Leontiadis; Kiwan Maeng; Mani Malek 001; Luca Melis; Ilya Mironov; Milad Nasr; Kaikai Wang; Carole-Jean Wu (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)

Chasing Carbon: The Elusive Environmental Footprint of Computing. - Udit Gupta; Young Geun Kim; Sylvia Lee; Jordan Tse; Hsien-Hsin S. Lee; Gu-Yeon Wei; David Brooks 001; Carole-Jean Wu (IEEE Micro, 2022)

CAMDNN: Content-Aware Mapping of a Network of Deep Neural Networks on Edge MPSoCs. - Soroush Heidari; Mehdi Ghasemi 003; Young Geun Kim; Carole-Jean Wu; Sarma B. K. Vrudhula (IEEE Trans. Computers, 2022)

EdgeWise: Energy-efficient CNN Computation on Edge Devices under Stochastic Communication Delays. - Mehdi Ghasemi 003; Daler N. Rakhmatov; Carole-Jean Wu; Sarma B. K. Vrudhula (ACM Trans. Embed. Comput. Syst., 2022)

Carbon Explorer: A Holistic Framework for Designing Carbon Aware Datacenters. - Bilge Acun; Benjamin Lee; Fiodar Kazhamiaka; Kiwan Maeng; Udit Gupta; Manoj Chakkaravarthy; David Brooks 001; Carole-Jean Wu (ASPLOS (2), 2023)

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)

Carbon-Efficient Design Optimization for Computing Systems. - Mariam Elgamal; Doug Carmean; Elnaz Ansari; Okay Zed; Ramesh Peri; Srilatha Manne; Udit Gupta; Gu-Yeon Wei; David Brooks 001; Gage Hills; Carole-Jean Wu (HotCarbon, 2023)

RecD: Deduplication for End-to-End Deep Learning Recommendation Model Training Infrastructure. - Mark Zhao; Dhruv Choudhary; Devashish Tyagi; Ajay Somani; Max Kaplan; Sung-Han Lin; Sarunya Pumma; Jongsoo Park; Aarti Basant; Niket Agarwal; Carole-Jean Wu; Christos Kozyrakis (MLSys, 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)

Tectonic-Shift: A Composite Storage Fabric for Large-Scale ML Training. - Mark Zhao; Satadru Pan; Niket Agarwal; Zhaoduo Wen; David Xu 010; Anand Natarajan; Pavan Kumar; Shiva Shankar P.; Ritesh Tijoriwala; Karan Asher; Hao Wu; Aarti Basant; et al. (USENIX ATC, 2023)

FlexShard: Flexible Sharding for Industry-Scale Sequence Recommendation Models. - Geet Sethi; Pallab Bhattacharya; Dhruv Choudhary; Carole-Jean Wu; Christos Kozyrakis (CoRR, 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)

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)

GreenScale: Carbon-Aware Systems for Edge Computing. - Young Geun Kim; Udit Gupta; Andrew McCrabb; Yonglak Son; Valeria Bertacco; David Brooks 001; Carole-Jean Wu (CoRR, 2023)

Design Space Exploration and Optimization for Carbon-Efficient Extended Reality Systems. - Mariam Elgamal; Doug Carmean; Elnaz Ansari; Okay Zed; Ramesh Peri; Srilatha Manne; Udit Gupta; Gu-Yeon Wei; David Brooks 001; Gage Hills; Carole-Jean Wu (CoRR, 2023)

READ: Recurrent Adaptation of Large Transformers. - Sid Wang; John Nguyen; Ke Li; Carole-Jean Wu (CoRR, 2023)

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 (CoRR, 2023)

GEVO-ML: Optimizing Machine Learning Code with Evolutionary Computation. - Jhe-Yu Liou; Stephanie Forrest; Carole-Jean Wu (CoRR, 2023)

Carbon Responder: Coordinating Demand Response for the Datacenter Fleet. - Jiali Xing; Bilge Acun; Aditya Sundarrajan; David Brooks 001; Manoj Chakkaravarthy; Nikky Avila; Carole-Jean Wu; Benjamin C. Lee (CoRR, 2023)

Data Acquisition: A New Frontier in Data-centric AI. - Lingjiao Chen; Bilge Acun; Newsha Ardalani; Yifan Sun; 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)

Generative AI Beyond LLMs: System Implications of Multi-Modal Generation. - Alicia Golden; Samuel Hsia; Fei Sun; Bilge Acun; Basil Hosmer; Yejin Lee 010; Zachary DeVito; Jeff Johnson 004; Gu-Yeon Wei; David Brooks 001; Carole-Jean Wu (CoRR, 2023)

Federated Ensemble Learning: Increasing the Capacity of Label Private Recommendation Systems. - Meisam Hejazinia; Dzmitry Huba; Ilias Leontiadis; Kiwan Maeng; Mani Malek 001; Luca Melis; Ilya Mironov; Milad Nasr; Kaikai Wang; Carole-Jean Wu (IEEE Data Eng. Bull., 2023)

Architectural CO2 Footprint Tool: Designing Sustainable Computer Systems With an Architectural Carbon Modeling Tool. - Udit Gupta; Mariam Elgamal; Gage Hills; Gu-Yeon Wei; Hsien-Hsin S. Lee; David Brooks 001; Carole-Jean Wu (IEEE Micro, 2023)

Special Issue on Environmentally Sustainable Computing. - Carole-Jean Wu (IEEE Micro, 2023)

Croissant: A Metadata Format for ML-Ready Datasets. - Mubashara Akhtar; Omar Benjelloun; Costanza Conforti; Pieter Gijsbers; Joan Giner-Miguelez; Nitisha Jain; Michael Kuchnik; Quentin Lhoest; Pierre Marcenac; Manil Maskey; Peter Mattson; Luis Oala; et al. (DEEM@SIGMOD, 2024)

HeteroSwitch: Characterizing and Taming System-Induced Data Heterogeneity in Federated Learning. - Gyudong Kim; Mehdi Ghasemi 003; Soroush Heidari; Seungryong Kim; Young Geun Kim; Sarma B. K. Vrudhula; Carole-Jean Wu (MLSys, 2024)

CHAI: Clustered Head Attention for Efficient LLM Inference. - Saurabh Agarwal; Bilge Acun; Basil Hosmer; Mostafa Elhoushi; Yejin Lee 010; Shivaram Venkataraman; Dimitris Papailiopoulos; Carole-Jean Wu (CoRR, 2024)

Croissant: A Metadata Format for ML-Ready Datasets. - Mubashara Akhtar; Omar Benjelloun; Costanza Conforti; Joan Giner-Miguelez; Nitisha Jain; Michael Kuchnik; Quentin Lhoest; Pierre Marcenac; Manil Maskey; Peter Mattson; Luis Oala; Pierre Ruyssen; et al. (CoRR, 2024)

Introducing v0.5 of the AI Safety Benchmark from MLCommons. - Bertie Vidgen; Adarsh Agrawal; Ahmed M. Ahmed 004; Victor Akinwande; Namir Al-Nuaimi; Najla Alfaraj; Elie Alhajjar; Lora Aroyo; Trupti Bavalatti; Borhane Blili-Hamelin; Kurt D. Bollacker; Rishi Bomassani; et al. (CoRR, 2024)

LayerSkip: Enabling Early Exit Inference and Self-Speculative Decoding. - Mostafa Elhoushi; Akshat Shrivastava; Diana Liskovich; Basil Hosmer; Bram Wasti; Liangzhen Lai; Anas Mahmoud 002; Bilge Acun; Saurabh Agarwal; Ahmed Roman; Ahmed A Aly; Beidi Chen; et al. (CoRR, 2024)

Is Flash Attention Stable? - Alicia Golden; Samuel Hsia; Fei Sun; Bilge Acun; Basil Hosmer; Yejin Lee 010; Zachary DeVito; Jeff Johnson 004; Gu-Yeon Wei; David Brooks 001; Carole-Jean Wu (CoRR, 2024)

Unlocking the Potential of Renewable Energy Through Curtailment Prediction. - Bilge Acun; Brent Morgan; Henry Richardson; Nat Steinsultz; Carole-Jean Wu (CoRR, 2024)

Beyond Efficiency: Scaling AI Sustainably. - Carole-Jean Wu; Bilge Acun; Ramya Raghavendra; Kim M. Hazelwood (CoRR, 2024)