Bilge Acun

Bilge Acun is a Research Scientist at Facebook AI Research (FAIR). Her research interests include Systems for Machine Learning, Parallel and Distributed Computing, Energy Efficient Computing. She is working on making large-scale machine learning systems fast and efficient. Particularly, she works on two optimization areas to improve the system throughput and efficiency:System and hardware optimizations using accelerators i.e. GPUs / TPUs and Algorithmic methods such as tensor compression.

Bilge received her Ph.D. degree in 2017 at the Department of Computer Science at Universtiy of Illinois at Urbana-Champaign. Her dissertation received the 2018 ACM SigHPC Dissertation Award Honorable Mention. Before joining Facebook, she worked at the IBM Thomas J. Watson Research Center as a Research Staff Member.

Bilge's Publications

SYSTEMS RESEARCH

RANKING & RECOMMENDATIONS

Understanding Training Efficiency of Deep Learning Recommendation Models at Scale

The use of GPUs has proliferated for machine learning workflows and is now considered mainstream for many deep learning models. Meanwhile, when training state-of-the-art personal recommendation models, which consume the highest number of compute cycles…

Bilge Acun, Matthew Murphy, Xiaodong Wang, Jade Nie, Carole-Jean Wu, Kim Hazelwood