计算机科学
重新使用
服务器
冗余(工程)
延迟(音频)
近似字符串匹配
计算
体验质量
高效能源利用
目标检测
匹配(统计)
实时计算
嵌入式系统
计算机工程
模式匹配
人工智能
计算机网络
服务质量
操作系统
模式识别(心理学)
工程类
算法
电气工程
统计
电信
废物管理
数学
作者
Wenquan Xu,Haoyu Song,Linyang Hou,Hui Zheng,Xinggong Zhang,Chuwen Zhang,Wei Hu,Yi Wang,Bin Liu
出处
期刊:International Conference on Computer Communications
日期:2021-05-10
被引量:1
标识
DOI:10.1109/infocom42981.2021.9488833
摘要
Offloading the 3D object detection from autonomous vehicles to MEC is appealing because of the gains on quality, latency, and energy. However, detection requests lead to repetitive computations since the multitudinous requests share approximate detection results. It is crucial to reduce such fuzzy redundancy by reusing the previous results. A key challenge is that the requests mapping to the reusable result are only similar but not identical. An efficient method for similarity matching is needed to justify the use case. To this end, by taking advantage of TCAM’s ap-proximate matching capability and NMC’s computing efficiency, we design SODA, a first-of-its-kind hardware accelerator which sits in the mobile base stations between autonomous vehicles and MEC servers. We design efficient feature encoding and partition algorithms for SODA to ensure the quality of the similarity matching and result reuse. Our evaluation shows that SODA significantly improves the system performance and the detection results exceed the accuracy requirements on the subject matter, qualifying SODA as a practical domain-specific solution.
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