CCRI: Planning: Collaborative Research: Planning to Develop a Low-Power Computer Vision Platform to Enhance Research in Computing Systems
CCRI:规划:协作研究:规划开发低功耗计算机视觉平台以加强计算系统研究
基本信息
- 批准号:1925514
- 负责人:
- 金额:$ 4.5万
- 依托单位:
- 依托单位国家:美国
- 项目类别:Standard Grant
- 财政年份:2019
- 资助国家:美国
- 起止时间:2019-10-01 至 2020-09-30
- 项目状态:已结题
- 来源:
- 关键词:
项目摘要
This project is a planning grant to investigate how to develop a research infrastructure for investigating energy-efficient computer vision. Computer vision is a set of technologies for understanding image or video. These technologies are essential in many applications, such as autonomous vehicles and airport security. Some vision systems need to be energy-efficient because they use batteries, for example, drones and mobile phones. This infrastructure is planned to be available for researchers to experiment and evaluate their solutions. The evaluation includes the success rates of identifying different types of objects, the speed, and the energy consumption. The infrastructure has three major components: (1) video taken by drones, (2) vision system analyzing the data, (3) power meter. It will be designed for a wide range of vision problems. It will also include a referee system which automates the entire evaluation process. There are many beneficial applications for low-power computer vision, for example, lightweight cameras that may provide safety for the vision impaired, wearable cameras that may improve factory safety or perform inventory analysis, and cameras for observing wildlife. The planning grant is to assess the needs of such an infrastructure for academia and industry. This team will actively engage the research community.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.
该项目是一项计划拨款,旨在研究如何开发研究节能计算机视觉的研究基础设施。计算机视觉是一套用于理解图像或视频的技术。这些技术在许多应用中都是必不可少的,比如自动驾驶汽车和机场安全。一些视觉系统需要节能,因为它们使用电池,例如无人机和移动电话。该基础设施计划供研究人员试验和评估他们的解决方案。评估包括识别不同类型物体的成功率、速度和能耗。基础设施有三个主要组成部分:(1)无人机拍摄的视频,(2)分析数据的视觉系统,(3)功率计。它将被设计用于广泛的视力问题。它还将包括一个裁判系统,使整个评估过程自动化。低功耗计算机视觉有许多有益的应用,例如,可以为视力受损者提供安全的轻型相机,可以提高工厂安全性或执行库存分析的可穿戴相机,以及用于观察野生动物的相机。规划拨款是为了评估学术界和工业界对这种基础设施的需求。该团队将积极参与研究社区。该奖项反映了美国国家科学基金会的法定使命,并通过使用基金会的知识价值和更广泛的影响审查标准进行评估,被认为值得支持。
项目成果
期刊论文数量(0)
专著数量(0)
科研奖励数量(0)
会议论文数量(0)
专利数量(0)
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Yiran Chen其他文献
Coca-Cola in process of materialisation: a new materialist perspective on He Xiangyu’s Cola Project
物化过程中的可口可乐:新唯物主义视角何翔宇的可乐计划
- DOI:
10.1080/21500894.2023.2196275 - 发表时间:
2023 - 期刊:
- 影响因子:0
- 作者:
Yiran Chen - 通讯作者:
Yiran Chen
Improving Multilevel Writes on Vertical 3-D Cross-Point Resistive Memory
改进垂直 3D 交叉点电阻存储器的多级写入
- DOI:
10.1109/tcad.2020.3006188 - 发表时间:
2021-04 - 期刊:
- 影响因子:2.9
- 作者:
Chengning Wang;Dan Feng;Wei Tong;Yu Hua;Jingning Liu;Bing Wu;Wei Zhao;Linghao Song;Yang Zhang;Jie Xu;Xueliang Wei;Yiran Chen - 通讯作者:
Yiran Chen
Shift-Optimized Energy-Efficient Racetrack-Based Main Memory
基于移位优化的节能赛道主存储器
- DOI:
10.1142/s0218126618500810 - 发表时间:
2017-09 - 期刊:
- 影响因子:0
- 作者:
王党辉;马浪;张萌;安建峰;Hai Helen Li;Yiran Chen - 通讯作者:
Yiran Chen
TriZone: A Design of MLC STT-RAM Cache for Combined Performance, Energy, and Reliability Optimizations
TriZone:MLC STT-RAM 缓存设计,可实现性能、能耗和可靠性的综合优化
- DOI:
10.1109/tcad.2017.2783860 - 发表时间:
2018-10 - 期刊:
- 影响因子:2.9
- 作者:
Zitao Liu;Mengjie Mao;Tao Liu;Xue Wang;WUjie Wen;Yiran Chen;Hai Li;王党辉;Yukui Pei;Ning Ge - 通讯作者:
Ning Ge
Yiran Chen的其他文献
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{{ truncateString('Yiran Chen', 18)}}的其他基金
Conference: 2023 CISE Computer System Research PI Meeting
会议:2023 CISE计算机系统研究PI会议
- 批准号:
2341163 - 财政年份:2023
- 资助金额:
$ 4.5万 - 项目类别:
Standard Grant
Collaborative Research: FuSe: Efficient Situation-Aware AI Processing in Advanced 2-Terminal SOT-MRAM
合作研究:FuSe:先进 2 端子 SOT-MRAM 中的高效态势感知 AI 处理
- 批准号:
2328805 - 财政年份:2023
- 资助金额:
$ 4.5万 - 项目类别:
Continuing Grant
Workshop Proposal: Redefining the Future of Computer Architecture from First Principles
研讨会提案:从第一原理重新定义计算机架构的未来
- 批准号:
2220601 - 财政年份:2022
- 资助金额:
$ 4.5万 - 项目类别:
Standard Grant
Collaborative Research: CCRI:NEW: Research Infrastructure for Real-Time Computer Vision and Decision Making via Mobile Robots
合作研究:CCRI:新:通过移动机器人进行实时计算机视觉和决策的研究基础设施
- 批准号:
2120333 - 财政年份:2021
- 资助金额:
$ 4.5万 - 项目类别:
Standard Grant
AI Institute for Edge Computing Leveraging Next Generation Networks (Athena)
利用下一代网络的人工智能边缘计算研究所 (Athena)
- 批准号:
2112562 - 财政年份:2021
- 资助金额:
$ 4.5万 - 项目类别:
Cooperative Agreement
EAGER: Distributed Heterogeneous Data Analytics via Federated Learning
EAGER:通过联邦学习进行分布式异构数据分析
- 批准号:
2140247 - 财政年份:2021
- 资助金额:
$ 4.5万 - 项目类别:
Standard Grant
Collaborative Research: SHF: Medium: Revitalizing EDA from a Machine Learning Perspective
合作研究:SHF:媒介:从机器学习的角度振兴 EDA
- 批准号:
2106828 - 财政年份:2021
- 资助金额:
$ 4.5万 - 项目类别:
Standard Grant
Collaborative Research: Two-dimensional Synaptic Array for Advanced Hardware Acceleration of Deep Neural Networks
合作研究:用于深度神经网络高级硬件加速的二维突触阵列
- 批准号:
1955246 - 财政年份:2020
- 资助金额:
$ 4.5万 - 项目类别:
Standard Grant
Workshop Proposal: Processing-In-Memory (PIM) Technology - Grand Challenges and Applications
研讨会提案:内存处理 (PIM) 技术 - 重大挑战和应用
- 批准号:
2027324 - 财政年份:2020
- 资助金额:
$ 4.5万 - 项目类别:
Standard Grant
RTML: Large: Collaborative: Harmonizing Predictive Algorithms and Mixed Signal/Precision Circuits via Computation-Data Access Exchange and Adaptive Dataflows
RTML:大型:协作:通过计算数据访问交换和自适应数据流协调预测算法和混合信号/精密电路
- 批准号:
1937435 - 财政年份:2019
- 资助金额:
$ 4.5万 - 项目类别:
Standard Grant
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