September 20, 2019

Systems for Machine Learning request for proposals

About

This Research Award is now closed

Machine learning (ML) methods have been widely applied to a large variety of day-to-day services hosted by Facebook. These services include personal recommendation, content understanding (language translation, vision, and speech), integrity, and more. As Facebook increasingly relies on ML systems to better address the individual needs of users, we also invest in full-stack and end-to-end optimization in our infrastructure to guarantee the quality of services in performance, availability, accuracy, and reliability. Such high QoS is achieved holistically across the entire computing stack from robust machine learning development frameworks down to maximizing compute/power efficiency in data centers. The breadth of this endeavor is often beyond internal research and development, and includes outreach to and collaboration with research communities in academia.

To sustain traditional computing at scale for ever-growing machine learning workloads in our data centers, we continue to call for impactful solutions in the areas of developer toolkits, compilers/code generation, system architecture, memory technologies, and ML accelerator support. In addition, emerging issues around compute trustworthiness and data privacy have become forthcoming challenges and require interdisciplinary innovation between system engineers and privacy/security scientists. We are looking forward to novel system design approaches to enable privacy-preserving machine learning, differential privacy, or multi-party compute. Finally, we are interested in exploring any disruptive, game-changing technology for the landscape of training and inference for machine learning.

To foster further innovation and to deepen our collaboration with academia, Facebook is pleased to invite faculty to respond to this call for research proposals pertaining to the aforementioned topics. Applicants should submit a maximum two-page proposal detailing what contribution their research is expected to make, how the research domain will benefit from the work, 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 interdisciplinary teams with complementary expertise are also encouraged. We anticipate awarding six to nine proposals, in the range of $50,000 to $100,000 with larger awards for interdisciplinary collaborations between multiple PIs. Payment will be made to the proposer’s host university as an unrestricted gift.

This year, we are particularly interested in proposals that fall into these categories:

  • Scalable, elastic, and reliable distributed machine learning and inference
  • System and architecture support for personalized recommendation systems
  • Programming language and compilers for platform-agnostic machine learning
  • Resource provisioning for efficient inference/training in heterogeneous data centers
  • On-device training and inference
  • System and architecture support for privacy-preserving machine learning
  • System support for multi-party compute and private/secure inference
  • Emerging technologies, such as near-memory processing and in-memory computing systems applied to machine learning
  • Novel machine learning systems beyond neural networks
  • Emerging technologies for efficient machine learning

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
  • Organization details. This will include tax information and administrative contact details

Eligibility

  • 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.

Timing and dates

  • Applications are now closed.
  • Notifications will be sent by email to selected applicants by December 2019.

Terms and Conditions

  • By submitting a proposal, you are authorizing Facebook to evaluate the proposal for a potential award, and you agree to the terms herein.
  • You agree that Facebook will not be required to treat any part of the proposal as confidential or protected by copyright, and may use, edit, modify, copy, reproduce, and distribute all or a portion of your proposal in any manner for the sole purposes of administering the website and evaluating the contents of the proposal.
  • You agree and acknowledge that personal data submitted with the proposal, including name, mailing address, phone number, and email address of you and other named researchers in the proposal may be collected, processed, stored and otherwise used by Facebook for the purposes of administering the website and evaluating the contents of the proposal.
  • You acknowledge that neither party is obligated to enter into any business transaction as a result of the proposal submission, Facebook is under no obligation to review or consider the proposal, and neither party acquires any intellectual property rights as a result of submitting the proposal.
  • Any feedback you provide to Facebook in the proposal regarding its products or services will not be treated as confidential or protected by copyright, and Facebook is free to use such feedback on an unrestricted basis with no compensation to you.

 

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