Priority-based parameter propagation for distributed DNN training A Jayarajan, J Wei, G Gibson, A Fedorova, G Pekhimenko Proceedings of Machine Learning and Systems 19, 2019 | 186 | 2019 |
Benchmarking and Analyzing Deep Neural Network Training H Zhu, M Akrout, B Zheng, A Pelegris, A Jayarajan, A Phanishayee, ... 2018 IEEE International Symposium on Workload Characterization (IISWC), 88-100, 2018 | 143 | 2018 |
Fpraker: A processing element for accelerating neural network training OM Awad, M Mahmoud, I Edo, AH Zadeh, C Bannon, A Jayarajan, ... MICRO-54: 54th Annual IEEE/ACM International Symposium on Microarchitecture …, 2021 | 18 | 2021 |
How to validate Machine Learning Models Prior to Deployment: Silent trial protocol for evaluation of real-time models at ICU S Tonekaboni, G Morgenshtern, A Assadi, A Pokhrel, X Huang, ... Conference on Health, Inference, and Learning, 169-182, 2022 | 9 | 2022 |
LifeStream: A High-performance Stream Processing Engine for Periodic Streams A Jayarajan, K Hau, A Goodwin, G Pekhimenko 26th ACM International Conference on Architectural Support for Programming …, 2021 | 7* | 2021 |
TiLT: A Time-Centric Approach for Stream Query Optimization and Parallelization A Jayarajan, W Zhao, Y Sun, G Pekhimenko Proceedings of the 28th ACM International Conference on Architectural …, 2023 | 5 | 2023 |
Arbitor: A Numerically Accurate Hardware Emulation Tool for {DNN} Accelerators C Jiang, A Jayarajan, H Lu, G Pekhimenko 2023 USENIX Annual Technical Conference (USENIX ATC 23), 519-536, 2023 | 1 | 2023 |
Hardware Sensitivity Analysis for Deep Learning Models A Jayarajan, G Pekhimenko, GA Gibson | | 2018 |
DNN Training Performance Analysis: A Divide and Conquer Approach A Jayarajan | | |
Training Larger Models on TensorFlow without Additional GPU J Wei, A Qiao, A Jayarajan, G Gibson, V Vasudevan, E Xing | | |
FOMG: Few-shOt Music Generation A Jayarajan, HY Ling, M Suhail | | |
TBD SUITE: BENCHMARKING AND PROFILING TOOLS FOR DNNS XY Geoffrey, H Zhu, A Jayarajan, B Zheng, A Tiwari, G Pekhimenko | | |