In light of the COVID-19 outbreak, we have extended the deadline for this RFP from Wednesday, April 15 to Thursday, April 30 to give research teams additional time to submit proposals and focus first on care for their families, friends, and communities. We also understand that universities may experience some challenges regarding support for grant programs now and in the coming months. We will continue to work closely with research teams and universities on this.
In January 2019, Facebook invited university faculty to respond to a call for research proposals on AI System Hardware/Software Co-Design. We received more than 80 submissions, many of which provided promising research directions. We were honored to be able to support and award eight of those proposals. These winners were invited to our annual AI Systems Faculty Summit at Facebook Menlo Park headquarters.
This year at MLSys, Facebook is pleased to invite university faculty to respond to the new call for research proposals on AI System Hardware/Software Co-Design. Deep learning has been particularly amenable to simultaneous design and optimization of several aspects of the system, including hardware and software, to achieve a set target for a given system metric, such as throughput, latency, power, size, or their combination. Facebook AI teams have been using co-design to develop high-performance AI solutions for both existing as well as future AI hardware, and we are currently looking to further explore co-design opportunities across a number of new dimensions.
Facebook is especially interested in soliciting proposals for the wide range of AI hardware/algorithm co-design research areas, including but not limited to:
- Recommendation models
- Compression, quantization, pruning techniques
- Graph-based systems with implications on hardware (graph learning)
- Hardware/software co-design for deep learning
- Energy-efficient hardware architectures
- Hardware efficiency-aware neural architecture search
- Mixed-precision linear algebra and tensor-based frameworks
- Distributed training
- Software frameworks for efficient use of programmable hardware
- Scalable communication-aware and data movement-aware algorithms
- High-performance and fault-tolerant communication middleware
- High-performance fabric topology and network transport for distributed training
- Performance, programmability, and efficiency at data center scale
- Machine learning-driven data access optimization (e.g., prefetching and caching)
- Enabling large model deployment through intelligent memory and storage
- Training un/self/semi-supervised models on large scale video data sets
Applicants should submit a proposal detailing what contribution their research is expected to make, how the research domain will benefit from the work, project timeline, and a budget overview of how the proposed funding will be used.
Proposals are highly encouraged to focus funding on project personnel, especially PhD students. Proposals from small collaborative teams, particularly with Principal Investigators bridging areas of systems and machine learning, are also encouraged. A total of six awards are available, up to $50,000 each, depending on the specific requirements. Payment will be made to the proposer’s host university as an unrestricted gift.
Proposals should include
- A summary of the project (1–2 pages) explaining the area of focus, a description of techniques, any relevant prior work, and a timeline with milestones and expected outcomes
- A draft budget description (1 page) including an approximate cost of the award and explanation of how funds would be spent
- Curriculum Vitae for all project participants, including PhD students
- Organization details; this will include tax information and administrative contact details
- Awards must comply with applicable U.S. and international laws, regulations, and policies.
- Applicants must be current full-time faculty at an accredited academic institution that awards research degrees to PhD students.
- Applicants must be the Principal Investigator on any resulting award.
- Applicants must be a resident of and academic institution must be located in the Territory (defined below).
“Territory” is defined any area, country, state, territory, or province where applicable laws do not prohibit applying for or receiving a grant in this RFP and excludes China, Cuba, Crimea, Iran, North Korea, Sudan, Myanmar/Burma, Syria, Zimbabwe, Iraq, Lebanon, Liberia, Libya, Somalia, Zimbabwe, Belarus, and any other area or country designated by the applicable agency that designates trade sanctions. Government officials, political figures, and businesses politically affiliated (all as determined by Facebook in its sole discretion) are not eligible to apply for the grant.
Timing and dates
- Applications are now open. Deadline to apply is Thursday, April 30th, 2020 at 5:00 pm AOE.
- Notifications will be sent by email to selected applicants by June 30th, 2020.
Winners will be invited to the annual AI Systems Faculty Summit held in Fall 2020 (Menlo Park). Facebook will pay for the winners’ travel and accommodations (one representative per winning proposal) to attend and present during the summit.
We encourage the winners to openly publish any findings/insights from their work. Successful awardees will be listed on the Facebook Research website.
Frequently Asked Questions
Do you typically limit the salary of the PI in the gift?+
Should the proposal be double- or single-spaced? Is there any required/expected font?+
What is the award cycle or when does the funding year begin and end?+
Can award funds be used to cover a researcher's summer salary while conducting research?+
Can you please explain the budget breakdown in more detail?+
We are working as co-PIs and are at the same institution. Is it possible to list both of our names as PI for an RFP proposal?+