Research

Areas of interest include, but are not limited to, the following.

1. Recommendation models

• Compression, quantization, pruning, adaptive sparsity techniques
• Hardware-aware novel modeling techniques

2. 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

3. Distributed training

• Software frameworks for efficient use of programmable hardware
• Scalable communication-aware and data movement-aware algorithms
• High-performance and/or fault-tolerant communication middleware
• Systems/components for enabling high performance training of large-scale models (e.g., checkpointing, transfer learning, data reading, model publishing)

4. 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
• Meta-learning and continual learning techniques

Proposals should include

• A summary of the project (one to two pages), in English, 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 (one 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

• The proposal must comply with applicable U.S. and international laws, regulations, and policies.
• Applicants must be current faculty or employed in a role focused on research at an accredited academic institution, university, or non-profit organization. Students, including PhD students, are not eligible as applicants.
• Applicants must be the Principal Investigator on any resulting award.
• Meta cannot consider proposals submitted, prepared, or to be carried out by individuals residing in or affiliated with an academic institution, university, or non-profit located in a country or territory subject to comprehensive U.S. trade sanctions.
• Government officials (excluding faculty and staff of public universities, to the extent they may be considered government officials), political figures, and politically affiliated businesses (all as determined by Meta in its sole discretion) are not eligible.
• Applicants cannot be current employees or contractors at Meta or any of its affiliated brands.

Meta’s decisions will be final in all matters relating to Meta RFP solicitations, including whether or not to grant an award and the interpretation of Meta RFP Terms and Conditions. By submitting a proposal, applicants affirm that they have read and agree to these Terms and Conditions.

• Meta is authorized to evaluate proposals submitted under its RFPs, to consult with outside experts, as needed, in evaluating proposals, and to grant or deny awards using criteria determined by Meta to be appropriate and at Meta sole discretion. Meta’s decisions will be final in all matters relating to its RFPs, and applicants agree not to challenge any such decisions.
• Meta will not be required to treat any part of a proposal as confidential or protected by copyright, and may use, edit, modify, copy, reproduce and distribute all or a portion of the proposal in any manner for the sole purposes of administering the Meta RFP website and evaluating the contents of the proposal.
• Personal data submitted with a proposal, including name, mailing address, phone number, and email address of the applicant and other named researchers in the proposal may be collected, processed, stored and otherwise used by Meta for the purposes of administering Meta’s RFP website, evaluating the contents of the proposal, and as otherwise provided under Meta’s Privacy Policy.
• Neither Meta nor the applicant is obligated to enter into a business transaction as a result of the proposal submission. Meta is under no obligation to review or consider the proposal.
• Feedback provided in a proposal regarding Meta products or services will not be treated as confidential or protected by copyright, and Meta is free to use such feedback on an unrestricted basis with no compensation to the applicant. The submission of a proposal will not result in the transfer of ownership of any IP rights.
• Applicants represent and warrant that they have authority to submit a proposal in connection with a Meta RFP and to grant the rights set forth herein on behalf of their organization. All awards provided by Meta in connection with this RFP shall be used only in accordance with applicable laws and shall not be used in any way, directly or indirectly, to facilitate any act that would constitute bribery or an illegal kickback, an illegal campaign contribution, or would otherwise violate any applicable anti-corruption or political activities law.
• Funding for winning-RFP proposals will be provided to the academic institution with which the primary investigator/applicant is affiliated pursuant to a gift or other funding model as specified in the RFP call. Applicants understand and acknowledge that their affiliated academic institution will need to agree to the terms and conditions of such gift or other agreement to receive funding.
• Applicants acknowledge and agree that by submitting an application they are consenting to their name, university / organization’s name and proposal title being made public on Meta’s blog on the research.facebook.com website if they are chosen as an RFP winner or finalist. If an applicant is selected as a winner or finalist, they will then have the opportunity to provide written notification that they do not consent to the research.facebook.com blog inclusion.