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
May 19, 2021
SYSTEMS RESEARCH
TT-REC: Tensor Train Compression for Deep Learning Recommendation Model Embeddings
The memory capacity of embedding tables in deep learning recommendation models (DLRMs) is increasing dramatically from tens of GBs to TBs across the industry …
Chunxing Yin, Bilge Acun, Xing Liu, Carole-Jean Wu
May 19, 2021
February 27, 2021
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
February 27, 2021
