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Required Information | Responses (Please list N/A if not applicable) |
Name of Project | InfiniEdge AI |
Project Description (what it does, why it is valuable, origin and history) | InfiniEdge AI is a project that brings AI to the edge, enabling real-time AI inference. This technology extends the advantages of AI and Machine Learning to edge devices, thus aligning with the LF Edge Mission Statement. This technology can enhance applications in various sectors including manufacturing, telecommunications, healthcare, automotive (autonomous driving and smart cockpit), retail and etc. InfiniEdge AI originated as part of the AI Edge Blueprint Family (https://wiki.akraino.org/display/AK/The+AI+Edge+Blueprint+Family) and is based on the Shifu Framework (https://github.com/Edgenesis/shifu) and YoMo (https://github.com/yomorun/yomo)
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Statement on alignment with Foundation Mission Statement | InfiniEdge AI aligns with the LF Edge Mission Statement by creating an open, scalable, and interoperable framework for edge computing. This project embodies LF Edge's vision for edge applications by extending AI and Machine Learning benefits to edge devices. |
High level assessment of project synergy with existing projects under LF Edge, including how the project compliments/overlaps with existing projects, and potential ways to harmonize over time. Responses may be included both here and/or in accompanying documentation. | InfiniEdge AI enhances the overall LF Edge ecosystem by providing an AI/ML interface for edge devices. It does not overlap significantly with existing projects but brings unique capabilities to the table. Harmonization potential exists with IoT and edge computing-focused projects. |
Link to current Code of Conduct | N/A |
2 TAC Sponsors, if identified (Sponsors help mentor projects) - See full definition on Project Stages: Definitions and Expectations | Toshimichi Fukuda , Fujitsu; Tina Tsou , Arm |
Project license | Apache 2.0 |
Source control (GitHub by default) | GitHub |
Issue tracker (GitHub by default) | GitHub |
External dependencies (including licenses) |
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Release methodology and mechanics | |
Names of initial committers, if different from those submitting proposal | Liya Yu, Baidu Yu, Liya C.C., Allegro fanweixiao Jun Chen, Baidu Jun Chen Tom Qin, Edgenesis Tom Qin Yongli Chen, Edgenesis Kevin Zheng, Edgenesis Wenhui Zhang, Bytedance/TikTok Wenhui Zhang Joe Speed, Ampere Ray Chi, Advantech Roger Chen, SuperMicro Rick Cao, Meta Ashok Bhat, Arm Milos Puzovic, Arm Tina Tsou , InfiniEdge AI Qi Wang, Google |
Current number of code contributors to proposed project | 6 |
Current number of organizations contributing to proposed project | 4 (Baidu, Allegro, Edgenesis, TikTok) |
Briefly describe the project's leadership team and decision-making process | |
Advisors | Ranny Haiby Tina Tsou |
List of project's official communication channels (slack, irc, mailing lists) | N/A |
Link to project's website | N/A |
Links to social media accounts | N/A |
Existing financial sponsorship | N/A |
Infrastructure needs or requests (to include GitHub/Gerrit, CI/CD, Jenkins, Nexus, JIRA, other ...) | GitHub |
Currently Supported Architecture | x86-64, AArch64 |
Planned Architecture Support | N/A |
Project logo in svg format (see https://github.com/lf-edge/lfedge-landscape#logos for guidelines) | N/A |
Trademark status | N/A |
Does the project have a Core Infrastructure Initiative security best practices badge? (See: https://bestpractices.coreinfrastructure.org) | No |
Any additional information the TAC and Board should take into consideration when reviewing your proposal? | N/A |
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Work stream 1: Geo-distributed Cloud
- Leaders: Yona Cao C.C. Fan
- Objective: This work stream focuses on optimizing the performance and scalability of cloud infrastructure across multiple geographical locations. It aims to address challenges related to latency, data sovereignty, and efficiency in distributed computing environments.
- Approach: The team will develop strategies for seamless data synchronization and application performance across dispersed networks, ensuring robust, secure, and compliant operations globally.
Work stream 2: Edge Database
- Leader: Rick Cao Qi Wang
- Objective: The Edge Database work stream is dedicated to advancing database solutions tailored for edge computing environments. It targets improvements in data handling and storage capabilities on edge devices, enhancing local data processing and decision-making.
- Approach: Efforts will include the development of lightweight, scalable database systems that support real-time data processing and analytics, pivotal for Edge AI Virtual Agents.