Gennady Pekhimenko is a Canada CIFAR AI Chair at the Vector Institute and an assistant professor at the Department of Computer Science and the lead of the Ecosystem research group at the University of Toronto.
Pekhimenko’s research focuses on efficient memory hierarchy designs, systems for machine learning, approximate computing, compilers, and hardware acceleration.
- ISCA Hall of Fame, 2021
- IEEE MICRO Top Picks, 2020-2021
- HiPEAC Paper Award, 2020
- Amazon AWS Machine Learning Research Award, 2020-2021
- Facebook Faculty Research Award, 2020-2021
Reddi, V. J., Cheng, C., Kanter, D., Mattson, P., Schmuelling, G., Wu, C. J., … & Zhou, Y. (2020). Mlperf inference benchmark. In 2020 ACM/IEEE 47th Annual International Symposium on Computer Architecture (ISCA) (pp. 446-459). IEEE.
Mattson, P., Cheng, C., Coleman, C., Diamos, G., Micikevicius, P., Patterson, D., … & Zaharia, M. (2019). Mlperf training benchmark.
Jayarajan, A., Wei, J., Gibson, G., Fedorova, A., & Pekhimenko, G. (2019). Priority-based parameter propagation for distributed DNN training.
Hassan, H., Pekhimenko, G., Vijaykumar, N., Seshadri, V., Lee, D., Ergin, O., & Mutlu, O. (2016). ChargeCache: Reducing DRAM latency by exploiting row access locality. In 2016 IEEE International Symposium on High Performance Computer Architecture (HPCA) (pp. 581-593). IEEE.
Pekhimenko, G., Seshadri, V., Mutlu, O., Kozuch, M. A., Gibbons, P. B., & Mowry, T. C. (2012). Base-delta-immediate compression: Practical data compression for on-chip caches. In 2012 21st international conference on parallel architectures and compilation techniques (PACT) (pp. 377-388). IEEE.
CIFAR is a registered charitable organization supported by the governments of Canada, Alberta and Quebec, as well as foundations, individuals, corporations and Canadian and international partner organizations.