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2020 – today
- 2024
[c13]Jason Ansel, Edward Z. Yang, Horace He, Natalia Gimelshein, Animesh Jain, Michael Voznesensky, Bin Bao, Peter Bell
, David Berard, Evgeni Burovski, Geeta Chauhan, Anjali Chourdia, Will Constable, Alban Desmaison, Zachary DeVito, Elias Ellison, Will Feng, Jiong Gong, Michael Gschwind, Brian Hirsh, Sherlock Huang, Kshiteej Kalambarkar, Laurent Kirsch, Michael Lazos, Mario Lezcano, Yanbo Liang, Jason Liang, Yinghai Lu, C. K. Luk, Bert Maher, Yunjie Pan, Christian Puhrsch, Matthias Reso, Mark Saroufim, Marcos Yukio Siraichi, Helen Suk, Shunting Zhang, Michael Suo, Phil Tillet, Xu Zhao, Eikan Wang, Keren Zhou, Richard Zou, Xiaodong Wang, Ajit Mathews, William Wen, Gregory Chanan, Peng Wu, Soumith Chintala:
PyTorch 2: Faster Machine Learning Through Dynamic Python Bytecode Transformation and Graph Compilation. ASPLOS (2) 2024: 929-947- 2023
- [j1]Yogesh K. Dwivedi, Nir Kshetri, Laurie Hughes, Nripendra P. Rana, Abdullah M. Baabdullah, Arpan Kumar Kar, Alex Koohang, Samuel Ribeiro-Navarrete, Nina Belei, Janarthanan Balakrishnan, Sriparna Basu, Abhishek Behl, Gareth H. Davies, Vincent Dutot, Rohita Dwivedi, Leighton Evans, Reto Felix, Richard Foster-Fletcher, Mihalis Giannakis, Ashish Gupta, Chris Hinsch, Animesh Jain, Nina Jane Patel, Timothy Jung, Satinder Juneja, Qeis Kamran, Sanjar Mohamed AB, Neeraj Pandey, Savvas Papagiannidis, Ramakrishnan Raman, Philipp A. Rauschnabel, Preeti Tak, Alexandra Taylor, M. Claudia tom Dieck, Giampaolo Viglia, Yichuan Wang, Meiyi Yan:
Exploring the Darkverse: A Multi-Perspective Analysis of the Negative Societal Impacts of the Metaverse. Inf. Syst. Frontiers 25(5): 2071-2114 (2023) - [c12]Kaiqi Zhao, Animesh Jain, Ming Zhao:
Automatic Attention Pruning: Improving and Automating Model Pruning using Attentions. AISTATS 2023: 10470-10486 - [c11]Sanket Purandare, Abdul Wasay, Animesh Jain, Stratos Idreos:
μ-TWO: 3× Faster Multi-Model Training with Orchestration and Memory Optimization. MLSys 2023 - [c10]Krishnan Sukumar, Santosh Vodnala, Ravindra Ayyagari, Animesh Jain, Thanapandi Ganesan, Rajesh R:
Surmounting Challenges in the Design of Low Power Real Time Clock IP for Advanced FinFET Technology Nodes. VLSID 2023: 157-162 - [i7]Kaiqi Zhao, Animesh Jain, Ming Zhao:
Automatic Attention Pruning: Improving and Automating Model Pruning using Attentions. CoRR abs/2303.08595 (2023) - 2022
- [c9]Kaiqi Zhao, Hieu Nguyen, Animesh Jain, Nathan Susanj, Athanasios Mouchtaris, Lokesh Gupta, Ming Zhao:
Knowledge Distillation via Module Replacing for Automatic Speech Recognition with Recurrent Neural Network Transducer. INTERSPEECH 2022: 4436-4440 - [i6]Kaiqi Zhao, Animesh Jain, Ming Zhao:
Iterative Activation-based Structured Pruning. CoRR abs/2201.09881 (2022) - [i5]Kaiqi Zhao, Animesh Jain, Ming Zhao:
Adaptive Activation-based Structured Pruning. CoRR abs/2201.10520 (2022) - 2021
- [c8]Jian Weng, Animesh Jain, Jie Wang, Leyuan Wang, Yida Wang, Tony Nowatzki:
UNIT: Unifying Tensorized Instruction Compilation. CGO 2021: 77-89 - [i4]Jian Weng, Animesh Jain, Jie Wang, Leyuan Wang, Yida Wang, Tony Nowatzki:
UNIT: Unifying Tensorized Instruction Compilation. CoRR abs/2101.08458 (2021) - [i3]Ziheng Jiang, Animesh Jain, Andrew Liu, Josh Fromm, Chengqian Ma, Tianqi Chen, Luis Ceze:
Automated Backend-Aware Post-Training Quantization. CoRR abs/2103.14949 (2021) - 2020
- [i2]Hongbin Zheng, Sejong Oh, Huiqing Wang, Preston Briggs, Jiading Gai, Animesh Jain, Yizhi Liu, Rich Heaton, Randy Huang, Yida Wang:
Optimizing Memory-Access Patterns for Deep Learning Accelerators. CoRR abs/2002.12798 (2020) - [i1]Animesh Jain, Shoubhik Bhattacharya, Masahiro Masuda, Vin Sharma, Yida Wang:
Efficient Execution of Quantized Deep Learning Models: A Compiler Approach. CoRR abs/2006.10226 (2020)
2010 – 2019
- 2018
- [b1]Animesh Jain:
Addressing Memory Bottlenecks for Emerging Applications. University of Michigan, USA, 2018 - [c7]Animesh Jain, Michael A. Laurenzano, Gilles A. Pokam, Jason Mars, Lingjia Tang:
Architectural support for convolutional neural networks on modern CPUs. PACT 2018: 16:1-16:13 - [c6]Animesh Jain, Amar Phanishayee, Jason Mars, Lingjia Tang, Gennady Pekhimenko:
Gist: Efficient Data Encoding for Deep Neural Network Training. ISCA 2018: 776-789 - [c5]Ram Srivatsa Kannan, Animesh Jain, Michael A. Laurenzano, Lingjia Tang, Jason Mars:
Proctor: Detecting and Investigating Interference in Shared Datacenters. ISPASS 2018: 76-86 - 2017
- [c4]Parker Hill, Animesh Jain, Mason Hill, Babak Zamirai, Chang-Hong Hsu, Michael A. Laurenzano, Scott A. Mahlke, Lingjia Tang, Jason Mars:
DeftNN: addressing bottlenecks for DNN execution on GPUs via synapse vector elimination and near-compute data fission. MICRO 2017: 786-799 - 2016
- [c3]Animesh Jain, Michael A. Laurenzano, Lingjia Tang, Jason Mars:
Continuous shape shifting: Enabling loop co-optimization via near-free dynamic code rewriting. MICRO 2016: 23:1-23:12 - [c2]Animesh Jain, Parker Hill, Shih-Chieh Lin, Muneeb Khan, Md. Enamul Haque, Michael A. Laurenzano, Scott A. Mahlke, Lingjia Tang, Jason Mars:
Concise loads and stores: The case for an asymmetric compute-memory architecture for approximation. MICRO 2016: 41:1-41:13 - 2014
- [c1]Animesh Jain, Ritesh Parikh, Valeria Bertacco:
High-radix on-chip networks with low-radix routers. ICCAD 2014: 289-294
Coauthor Index
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