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Research
Latest papers
Federated benchmarking of medical artificial intelligence with MedPerf.
Nature machine intelligence · 2023
Latest funding
- $62,000
Computer Systems for Efficient and Scalable Machine Learning
NSERC · 2024 · Principal investigator
- $180,000
Efficient Compiler-Driven Pointer Compression
NSERC · 2019 · Principal investigator
- $281,967
Efficient Distributed DNN Training and Inference
NSERC · 2019 · Principal investigator
1 publications.
Federated benchmarking of medical artificial intelligence with MedPerf.
Karargyris A, Umeton R, Sheller MJ, Aristizabal A, George J, Wuest A, Pati S, Kassem H, Zenk M, Baid U, Narayana Moorthy P, Chowdhury A, Guo J, Nalawade S, Rosenthal J, Kanter D, Xenochristou M, Beutel DJ, Chung V, Bergquist T, Eddy J, Abid A, Tunstall L, Sanseviero O, Dimitriadis D, Qian Y, Xu X, Liu Y, Goh RSM, Bala S, Bittorf V, Reddy Puchala S, Ricciuti B, Samineni S, Sengupta E, Chaudhari A, Coleman C, Desinghu B, Diamos G, Dutta D, Feddema D, Fursin G, Huang X, Kashyap S, Lane N, Mallick I, FeTS Consortium, BraTS-2020 Consortium, AI4SafeChole Consortium, Mascagni P, Mehta V, Ferro Moraes C, Natarajan V, Nikolov N, Padoy N, Pekhimenko G, Reddi VJ, Reina GA, Ribalta P, Singh A, Thiagarajan JJ, Albrecht J, Wolf T, Miller G, Fu H, Shah P, Xu D, Yadav P, Talby D, Awad MM, Howard JP, Rosenthal M, Marchionni L, Loda M, Johnson JM, Bakas S, Mattson P
Computer Systems for Efficient and Scalable Machine Learning
Principal investigators: Pekhimenko, Gennady
Keywords: computer systems; compilers; computer architecture; optimization; deep neural networks; machine learning; distributed systems; memory systems
Efficient Compiler-Driven Pointer Compression
Principal investigators: Pekhimenko, Gennady
Efficient Distributed DNN Training and Inference
Principal investigators: Pekhimenko, Gennady
Exploiting Hardware Heterogeneity for Efficient Execution of Emerging Applications
Principal investigators: Pekhimenko, Gennady
Efficient Memory Footprint Reduction for Java Performance_x000d_ _x000d_
Principal investigators: Pekhimenko, Gennady
Exploiting Hardware Heterogeneity for Efficient Execution of Emerging Applications
Principal investigators: Pekhimenko, Gennady
Keywords: Approximate Computing; Computer Architecture; Computer Systems; Data Compression/Encoding; Deep Neural Network Training; Hardware Acceleration; Heterogeneous Systems; Performance and Energy Efficiency; Programming Models/Compilers
Exploiting Hardware Heterogeneity for Efficient Execution of Emerging Applications
Principal investigators: Pekhimenko, Gennady
NSERC COHESA: Computing Hardware for Emerging Intelligent Sensory Applications
Principal investigators: Moshovos, Andreas AM
From CIHR, NSERC and SSHRC funding decisions: CIHR since 2008, NSERC since 1991 and SSHRC since 1998, including their latest published competition results.
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