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SUMMARY:[ Facebook Research - Networking systems request for proposals | R
 esearch Funding ]
DTSTART;VALUE=DATE:20200203
DTSTAMP:20260406T194724Z
UID:5e67c832b8e91d96c27b5c1a0ce775de247a42648975bfe621f00fad
CATEGORIES:Call for proposal
DESCRIPTION:Aim: Facebook is pleased to invite university faculty to respo
 nd to this call for research proposals on enabling execution of artificial
  intelligence (AI) based capabilities within the constraints of edge devic
 es.\n\nAI has the potential to transform almost everything around us. It c
 an change the way humans interact with the world by making the objects aro
 und us “smart” — capable of constantly learning\, adapting\, and pro
 viding proactive assistance. The beginnings of this trend can already be s
 een in the new capabilities coming to Smartphones (speech assistant\, came
 ra night mode) as well as the new class of “smart” devices such as sma
 rt 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-ev
 erywhere” world.\n\nTo accelerate the transition towards a truly “smar
 t” world where AI capabilities permeate all devices and sensors\, Facebo
 ok is soliciting proposals on a wide range of topics related to efficient 
 on-device AI systems\, including\, but not limited to the following:\n\n	E
 xtending on-device capabilities for vision\, audio\, speech\, and natural 
 language processing\n	Distributing AI capabilities across the whole system
  stack from data capture at the edge to the cloud instead of performing al
 l the compute in the cloud\n	Machine learning techniques to optimize syste
 m tasks such as compression\, scheduling\, and caching\n	On-device privacy
 -preserving learning\n	Efficient machine learning models for edge devices\
 n	Dynamic neural networks\, such as mixture-of-expert networks\n	Platform-
 aware model optimization\n	Efficient hardware accelerator design\n	Tools f
 or architecture modeling\, design space exploration\, and algorithm mappin
 g\n	Efficient model execution on edge devices such as scheduling and tilin
 g\n	Emerging technologies such as near-sensor\, near-memory\, and near-sto
 rage computing\n\n\nFunding: max. USD $75’000\n\nEligibility:  Awards m
 ust comply with applicable U.S. and international laws\, regulations\, and
  policies. They must be current full-time faculty at an accredited academi
 c institution that awards research degrees to PhD students\, and they must
  be the Principal Investigator on any resulting award.\n\nHow to Apply: Ap
 plicants should submit a proposal detailing what contribution their resear
 ch is expected to make\, how the research domain will benefit from the wor
 k\, 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 t
 eams\, particularly with PIs bridging areas of systems and machine learnin
 g\, are also encouraged.\n\nProposals can be submitted on the application 
 portal\, and should include:\n\n\n	A summary of the project (1-2 pages) ex
 plaining the area of focus\, a description of techniques\, any relevant pr
 ior work\, and a timeline with milestones and expected outcomes\n	A draft 
 budget description (1 page) explaining how the funds would be spent\n	Name
 s of the researcher(s) involved in the proposed work\, their CV\, and any 
 previous or current connections/collaborations with Facebook\n	Organizatio
 nal details. This will include the host university\, tax information and a
 dministrative contact details. Contact the Research Office for help with t
 his step.\n\n\nDeadline: 03 February 2020 (11:59 pm PST)\n\nFurther inform
 ation\n\n\n	More details on the call can be found on the RFP website.\n	Fo
 r any other questions\, contact the Research Office.\n
LOCATION:
STATUS:CONFIRMED
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