Academia Industry Engagement: Difference between revisions
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==Bi-Weekly Meeting Invite== | ==Bi-Weekly Meeting Invite== | ||
11:00am - 11:55am PT Bi-Weekly Wednesday meetings (opposites weeks of the normal OCP-TAP meeting) : [https://teams.microsoft.com/dl/launcher/launcher.html?url=%2F_%23%2Fl%2Fmeetup-join%2F19%3Ameeting_OGE1MzNkMGMtZDRjMS00YzFiLTg4MTAtZjcxNzEwYzI2ODIy%40thread.v2%2F0%3Fcontext%3D%257b%2522Tid%2522%253a%2522fbb4e2fb-f802-4d55-beca-b7149551e928%2522%252c%2522Oid%2522%253a%2522cff27e02-befc-4437-98d5-96034f77b90c%2522%257d%26anon%3Dtrue&type=meetup-join&deeplinkId=5f0bbd41-a286-4e8b-b786-5162812cfe21&directDl=true&msLaunch=true&enableMobilePage=true&suppressPrompt=true Launch Teams Meeting Link] | 11:00am - 11:55am PT Bi-Weekly Wednesday meetings (opposites weeks of the normal OCP-TAP meeting) : [https://teams.microsoft.com/dl/launcher/launcher.html?url=%2F_%23%2Fl%2Fmeetup-join%2F19%3Ameeting_OGE1MzNkMGMtZDRjMS00YzFiLTg4MTAtZjcxNzEwYzI2ODIy%40thread.v2%2F0%3Fcontext%3D%257b%2522Tid%2522%253a%2522fbb4e2fb-f802-4d55-beca-b7149551e928%2522%252c%2522Oid%2522%253a%2522cff27e02-befc-4437-98d5-96034f77b90c%2522%257d%26anon%3Dtrue&type=meetup-join&deeplinkId=5f0bbd41-a286-4e8b-b786-5162812cfe21&directDl=true&msLaunch=true&enableMobilePage=true&suppressPrompt=true Launch Teams Meeting Link] | ||
==Zoom Details== | ==Zoom Details== |
Revision as of 19:16, 3 May 2024
Academic and Industry Engagement - Workstream #10
Back to Time Appliances Project's wiki
Goal
The Time Synchronization Industry-Academia Workstream (TSIAW) brings together researchers and industry practitioners to discuss and solve challenging problems in time synchronization of connected devices and applications.
Objectives
Bridge the gap between theory and practice
The TSIAW will act as a bridge between academic research and industry needs. By bringing together researchers and practitioners, the workstream will ensure that research efforts address real-world challenges faced by the ever-growing and complex data center and edge communication networks and devices by developing robust, reliable and scalable novel solutions
Synchronization Data for Decision Makers
Research and develop the data required by data center decision makers, on how to use precise time to make data center and edge applications (AI, Databases, Microservices, etc.) or portions of that software run 2x to 1000+x better in performance, power, latency and/or cost when compared to unsynchronized solution.
Develop Time Synchronization Projects
The TSIAW will expose projects for academia that will have impact on data center and edge technologies. When possible, TSIAW will help facilitating the research. When possible, making a community of time synchronization experts available to Academia and Industry to improve applications through time synchronization.
Advance the field of time synchronization
The TSIAW will strive to push the boundaries of time synchronization research by facilitating collaboration between industry and academia. This will involve identifying critical research areas, promoting joint research projects, and disseminating findings and solutions through conferences, workshops, repositories, and publications. (First virtually) (Not focused on crazy precise, more on applications)
Foster a dynamic, vibrant and knowledgeable community of time synchronization experts
The TSIAW will create a platform for communication and collaboration among industry professionals, academics, and researchers working on time synchronization
Project Team
- - Lead: Dan Biderman (Intel)
- - Lead: Hesham Albakoury (Individual)
Bi-Weekly Meeting Invite
11:00am - 11:55am PT Bi-Weekly Wednesday meetings (opposites weeks of the normal OCP-TAP meeting) : Launch Teams Meeting Link
Zoom Details
Meeting ID: 810 8874 0168
Passcode: 760037
Projects
Start Date | End Date | Call For Action | Statement | Introduction Slides | Wiki | |
---|---|---|---|---|---|---|
#1 | 5/1/2024 | Open | Artificial Intelligence / Machine Learning College Project | A Precisely Synchronized Datacenter Would Improve Overall Performance of Distributed Artificial Intelligence and Machine Learning | ||
References
Paper | Link | |
---|---|---|
#1 | TBD | TBD |
Demos
Demo Title | Video Link | |
---|---|---|
#1 | TBD | TBD |