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2020 – today
- 2024
[c16]Connor Imes
, Andrew Rittenbach, Peng Xie, Dong-In Kang, John Paul Walters, Stephen P. Crago:
Evaluating Deep Learning Recommendation Model Training Scalability with the Dynamic Opera Network. EuroMLSys@EuroSys 2024: 169-175- 2023
- [c15]Connor Imes, David W. King, John Paul Walters:
Distributed Edge Machine Learning Pipeline Scheduling with Reverse Auctions. FMEC 2023: 196-203 - [c14]Haonan Wang, Connor Imes, Souvik Kundu, Peter A. Beerel, Stephen P. Crago, John Paul Walters:
Quantpipe: Applying Adaptive Post-Training Quantization For Distributed Transformer Pipelines In Dynamic Edge Environments. ICASSP 2023: 1-5 - 2022
- [c13]Yang Hu, Connor Imes, Xuanang Zhao, Souvik Kundu, Peter A. Beerel, Stephen P. Crago, John Paul Walters:
PipeEdge: Pipeline Parallelism for Large-Scale Model Inference on Heterogeneous Edge Devices. DSD 2022: 298-307 - [c12]Ahsan Pervaiz, Yao-Hsiang Yang, Adam Duracz, Ferenc A. Bartha, Ryuichi Sai, Connor Imes, Robert Cartwright, Krishna V. Palem, Shan Lu, Henry Hoffmann:
GOAL: Supporting General and Dynamic Adaptation in Computing Systems. Onward! 2022: 16-32 - [i2]Haonan Wang, Connor Imes, Souvik Kundu, Peter A. Beerel, Stephen P. Crago, John Paul Walters:
QuantPipe: Applying Adaptive Post-Training Quantization for Distributed Transformer Pipelines in Dynamic Edge Environments. CoRR abs/2211.04515 (2022) - 2021
- [c11]Connor Imes, Tzu-Mao Li, Mark Glines, Rishi Khan, John Paul Walters:
Distributed and Heterogeneous SAR Backprojection with Halide. HPEC 2021: 1-9 - [i1]Yang Hu, Connor Imes, Xuanang Zhao, Souvik Kundu, Peter A. Beerel, Stephen P. Crago, John Paul Walters:
Pipeline Parallelism for Inference on Heterogeneous Edge Computing. CoRR abs/2110.14895 (2021) - 2020
- [c10]Connor Imes, Alexei Colin, Naifeng Zhang, Ajitesh Srivastava, Viktor K. Prasanna, John Paul Walters:
Compiler Abstractions and Runtime for Extreme-scale SAR and CFD Workloads. ESPM2@SC 2020: 1-7
2010 – 2019
- 2019
- [j2]Saeid Barati, Ferenc A. Bartha, Swarnendu Biswas, Robert Cartwright, Adam Duracz, Donald S. Fussell, Henry Hoffmann, Connor Imes, Jason E. Miller, Nikita Mishra, Arvind, Dung Nguyen, Krishna V. Palem, Yan Pei, Keshav Pingali, Ryuichi Sai, Andrew Wright, Yao-Hsiang Yang, Sizhuo Zhang:
Proteus: Language and Runtime Support for Self-Adaptive Software Development. IEEE Softw. 36(2): 73-82 (2019) - [c9]Connor Imes, Huazhe Zhang, Kevin Zhao, Henry Hoffmann:
CoPPer: Soft Real-Time Application Performance Using Hardware Power Capping. ICAC 2019: 31-41 - 2018
- [c8]Nikita Mishra, Connor Imes, John D. Lafferty, Henry Hoffmann:
CALOREE: Learning Control for Predictable Latency and Low Energy. ASPLOS 2018: 184-198 - [c7]Connor Imes, Steven A. Hofmeyr, Henry Hoffmann:
Energy-efficient Application Resource Scheduling using Machine Learning Classifiers. ICPP 2018: 45:1-45:11 - 2016
- [c6]Connor Imes, David H. K. Kim, Martina Maggio, Henry Hoffmann:
Portable Multicore Resource Management for Applications with Performance Constraints. MCSoC 2016: 305-312 - [c5]Connor Imes, Henry Hoffmann:
Bard: A unified framework for managing soft timing and power constraints. SAMOS 2016: 31-38 - [c4]Connor Imes, Lars Bergstrom, Henry Hoffmann:
A portable interface for runtime energy monitoring. SIGSOFT FSE 2016: 968-974 - 2015
- [c3]David H. K. Kim, Connor Imes, Henry Hoffmann:
Racing and Pacing to Idle: Theoretical and Empirical Analysis of Energy Optimization Heuristics. CPSNA 2015: 78-85 - [c2]Connor Imes, David H. K. Kim, Martina Maggio, Henry Hoffmann:
POET: a portable approach to minimizing energy under soft real-time constraints. RTAS 2015: 75-86 - 2014
- [j1]Connor Imes, Henry Hoffmann:
Minimizing energy under performance constraints on embedded platforms: resource allocation heuristics for homogeneous and single-ISA heterogeneous multi-cores. SIGBED Rev. 11(4): 49-54 (2014) - [c1]Connor Imes, Henry Hoffmann:
Minimizing Energy Under Performance Constraints on Embedded Platforms. EWiLi 2014
Coauthor Index
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