Facebook is pleased to invite university faculty to respond to this call for research proposals on enabling execution of artificial intelligence (AI) based capabilities within the constraints of edge devices.
AI has the potential to transform almost everything around us. It can change the way humans interact with the world by making the objects around us “smart” — capable of constantly learning, adapting, and providing proactive assistance. The beginnings of this trend can already be seen in the new capabilities coming to Smartphones (speech assistant, camera night mode) as well as the new class of “smart” devices such as smart watches, smart thermostats, and so on. However, the current class of “smart” devices run much of the computation on the cloud (or a remote host) — costing them transmission power and response latency as well as causing potential privacy concerns. This limits their ability to provide a compelling user experience and realize the true potential of an “AI-everywhere” world.
To accelerate the transition towards a truly “smart” world where AI capabilities permeate all devices and sensors, Facebook is soliciting proposals on a wide range of topics related to efficient on-device AI systems, including, but not limited to the following:
- Extending on-device capabilities for vision, audio, speech, and natural language processing
- Distributing AI capabilities across the whole system stack from data capture at the edge to the cloud instead of performing all the compute in the cloud
- Machine learning techniques to optimize system tasks such as compression, scheduling, and caching
- On-device privacy-preserving learning
- Efficient machine learning models for edge devices
- Dynamic neural networks, such as mixture-of-expert networks
- Platform-aware model optimization
- Efficient hardware accelerator design
- Tools for architecture modeling, design space exploration, and algorithm mapping
- Efficient model execution on edge devices such as scheduling and tiling
- Emerging technologies such as near-sensor, near-memory, and near-storage computing
Applicants should submit a proposal detailing what contribution their research is expected to make, how the research domain will benefit from the work, a 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 PIs bridging areas of systems and machine learning, are also encouraged.
A total of up to eight awards are available, up to $75,000 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) explaining how the funds would be spent
- Names of the researcher(s) involved in the proposed work, their CV, and any previous or current connections/collaborations with Facebook
- Organizational details. This will include the host university, 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.
Timing and dates
- Applications are now open. Deadline to apply is Monday, February 3, 2020 at 11:59 pm PST.
- Notifications will be sent by email to selected applicants by Friday, April 17, 2020.
Successful awardees will be listed on the Facebook Research website and will be encouraged to openly publish any findings/insights from their work.