Gennady Pekhimenko

Faculty Member

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Faculty Member, Vector Institute

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

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