This is a navigation window which overlays the main content of the page. Pressing the "X" at the top right corner of the modal will close the modal and bring you back to where you were on the page.
See posts about
This is a call to action window which overlays the main content of the page. Pressing the "X" at the top right corner of the modal will close the modal and bring you back to where you were on the page.
Let's Collaborate
This is a search window which overlays the main content of the page. Pressing the "X" at the top right corner of the modal will close the modal and bring you back to where you were on the page.
Associate Professor, Department of Computer Science, University of Toronto
Canada CIFAR Artificial Intelligence Chair
Gennady Pekhimenko is an Assistant Professor at the University of Toronto, CS department and (by courtesy) ECE department, where he is leading the EcoSystem (Efficient Computing Systems) group. Gennady is also a Faculty Member at Vector Institute and a CIFAR AI chair. Before joining Univ. of Toronto, he spent a year in 2017 at Microsoft Research in Redmond in the Systems Research group. He got his PhD from the Computer Science Department at Carnegie Mellon University in 2016. Gennady is a recipient of Amazon Machine Learning Research Award, Facebook Faculty Research Award, Connaught New Researcher Award, NVIDIA Graduate, Microsoft Research, Qualcomm Innovation, and NSERC CGS-D Fellowships. He was also recently inducted into the ISCA Hall of Fame and was a recipient of the MICRO Top Picks Award in 2021. His research interests are in the areas of systems, computer architecture, compilers, and applied machine learning.
Research Interests
Systems for Machine Learning
Machine Learning for Systems
Computer Architecture
Stream Processing
Highlights
ISCA Hall of Fame
MICRO Top Picks Award in 2021
Amazon Machine Learning Research Award
Facebook Faculty Research Award
Connaught New Researcher Award
Publications
Distributed deep learning in open collaborations
Michael Diskin and Alexey Bukhtiyarov and Max Ryabinin and Lucile Saulnier and Anton Sinitsin and Dmitry Popov and Dmitry V Pyrkin and Maxim Kashirin and Alexander Borzunov and Albert Villanova del Moral and Denis Mazur and Ilia Kobelev and Yacine Jernite and Thomas Wolf and Gennady Pekhimenko
2021
Moshpit SGD: Communication-efficient decentralized training on heterogeneous unreliable devices
Max Ryabinin and Eduard Gorbunov and Vsevolod Plokhotnyuk and Gennady Pekhimenko
2021
FPRaker: A Processing Element For Accelerating Neural Network Training
Omar Mohamed Awad and Mostafa Mahmoud and Isak Edo and Ali Hadi Zadeh and Ciaran Bannon and Anand Jayarajan and Gennady Pekhimenko and Andreas Moshovos
2021
LifeStream: a high-performance stream processing engine for periodic streams
Anand Jayarajan and Kimberly Hau and Andrew Goodwin and Gennady Pekhimenko
2021
NVOverlay: enabling efficient and scalable high-frequency snapshotting to NVM
Ziqi Wang and Chul-Hwan Choo and Michael A Kozuch and Todd C Mowry and Gennady Pekhimenko and Vivek Seshadri and Dimitrios Skarlatos
2021
Federated benchmarking of medical artificial intelligence with MedPerf
Alexandros Karargyris and Renato Umeton and Micah J Sheller and Alejandro Aristizabal and Johnu George and Anna Wuest and Sarthak Pati and Hasan Kassem and Maximilian Zenk and Ujjwal Baid and Prakash Narayana Moorthy and Alexander Chowdhury and Junyi Guo and Sahil Nalawade and Jacob Rosenthal and David Kanter and Maria Xenochristou and Daniel J Beutel and Verena Chung and Timothy Bergquist and James Eddy and Abubakar Abid and Lewis Tunstall and Omar Sanseviero and Dimitrios Dimitriadis and Yiming Qian and Xinxing Xu and Yong Liu and Rick Siow Mong Goh and Srini Bala and Victor Bittorf and Sreekar Reddy Puchala and Biagio Ricciuti and Soujanya Samineni and Eshna Sengupta and Akshay Chaudhari and Cody Coleman and Bala Desinghu and Gregory Diamos and Debo Dutta and Diane Feddema and Grigori Fursin and Xinyuan Huang and Satyananda Kashyap and Nicholas Lane and Indranil Mallick and FeTS Consortium and BraTS-2020 Consortium and AI4SafeChole Consortium and Pietro Mascagni and Virendra Mehta and Cassiano Ferro Moraes and Vivek Natarajan and Nikola Nikolov and Nicolas Padoy and Gennady Pekhimenko and Vijay Janapa Reddi and G Anthony Reina and Pablo Ribalta and Abhishek Singh and Jayaraman J Thiagarajan and Jacob Albrecht and Thomas Wolf and Geralyn Miller and Huazhu Fu and Prashant Shah and Daguang Xu and Poonam Yadav and David Talby and Mark M Awad and Jeremy P Howard and Michael Rosenthal and Luigi Marchionni and Massimo Loda and Jason M Johnson and Spyridon Bakas and Peter Mattson
2023
Speeding up fourier neural operators via mixed precision
Colin White and Renbo Tu and Jean Kossaifi and Gennady Pekhimenko and Kamyar Azizzadenesheli and Anima Anandkumar
2023
Arbitor: A Numerically Accurate Hardware Emulation Tool for {DNN} Accelerators
Chenhao Jiang and Anand Jayarajan and Hao Lu and Gennady Pekhimenko
2023
TorchProbe: Fuzzing Dynamic Deep Learning Compilers
Qidong Su and Chuqin Geng and Gennady Pekhimenko and Xujie Si
2023
Grape: Practical and Efficient Graphed Execution for Dynamic Deep Neural Networks on GPUs
Bojian Zheng and Cody Hao Yu and Jie Wang and Yaoyao Ding and Yizhi Liu and Yida Wang and Gennady Pekhimenko
2023
Guaranteed Approximation Bounds for Mixed-Precision Neural Operators
Renbo Tu and Colin White and Jean Kossaifi and Boris Bonev and Gennady Pekhimenko and Kamyar Azizzadenesheli and Anima Anandkumar
2023
Efficient data encoding for deep neural network training
Amar Phanishayee and Gennady Pekhimenko and Animesh Jain
We use cookies to ensure that we give you the best experience on our website. If you continue to use this site we will assume that you are happy with it.
