The engineering stack and infrastructure at Meta enable us to build communities and connect billions of people around the world. To support our AI workloads at scale in a proactive way, we are adopting a different approach to the design of our AI stack and the infrastructure it runs on.
Through this RFP, we aim to partner with academics interested in using AI and ML approaches, such as reinforcement learning, Bayesian modeling, and graph representation learning to automate and improve the whole AI stack: from silicon to AI models’ output.
The RFP attracted 30 proposals from 28 universities and institutions around the world. Thank you to everyone who took the time to submit, and congratulations to the winners.
Research award recipients
Principal investigators are listed first unless otherwise noted.
An AI-assisted system for sustainable and affordable AI
Zhihao Jia, Yue Zhao, Zhihao Zhang (Carnegie Mellon University)
Arch-gym: Benchmarking ML algorithms for parameter set architecture design
Vijay Janapa Reddi (Harvard University)
Bayesian learning for tomography diagnostics in fusion reactors
Matthieu Simeoni, Basil Duval, Christian Theiler (École Polytechnique Fédérale de Lausanne)
Creating a dataset for ML-guided chip design
Lizy Kurian John, Andreas Gerstlauer (University of Texas at Austin)
DRL-ORAN platform for large-scale networking resource management
Shih-Chun Lin, Brian Barritt (North Carolina State University)
Goal-oriented search for uncommon code optimizations in Halide
Riyadh Baghdadi (New York University)
Learning multi-objective policies for automated chip design
Aditya Grover, Tung Nguyen (University of California, Los Angeles)
Learning to fuzz tensor compilers
Lingming Zhang (University of Illinois Urbana-Champaign)
Optimizing deep learning clusters with transient capacity using AI
Maria Rodriguez Read, Aaron Harwood, Rajkumar Buyya (University of Melbourne)