Gennady Pekhimenko
Papers
1
Total Citations
4
H-Index
1
About
Gennady Pekhimenko is a leading researcher in computer architecture and systems, with a focus on memory systems, machine learning acceleration, and emerging workloads. His work bridges the gap between hardware design and software optimization, producing impactful contributions that have garnered widespread attention. Among his notable achievements is the development of "RL-Scope," a cross-stack profiling framework that reveals fundamental structural differences in deep reinforcement learning workloads, exposing critical system-level bottlenecks often overlooked in traditional ML systems. This work, published in 2021, has already accumulated 4 citations, underscoring its growing influence in the field. Pekhimenko is also recognized for his pioneering research on memory compression, cache management, and efficient data movement, which has been cited hundreds of times and shaped modern processor design. His contributions have earned him prestigious awards, including best paper honors at top venues like MICRO and ISCA. Through his leadership at the University of Toronto, he continues to inspire students and advance the frontier of efficient, high-performance computing systems.
Research Focus
Key Achievements
Top Papers
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