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2022 ◽  
Vol 50 ◽  
pp. 101805
Author(s):  
Deqiang He ◽  
Xiaoliang Teng ◽  
Yanjun Chen ◽  
Bin Liu ◽  
Jinxin Wu
Keyword(s):  

2022 ◽  
Vol 309 ◽  
pp. 118409
Author(s):  
Yongming Han ◽  
Jingze Li ◽  
Xiaoyi Lou ◽  
Chenyu Fan ◽  
Zhiqiang Geng

2022 ◽  
Vol 27 (2) ◽  
pp. 1-19
Author(s):  
Tiancong Bu ◽  
Kaige Yan ◽  
Jingweijia Tan

Dense SLAM is an important application on an embedded environment. However, embedded platforms usually fail to provide enough computation resources for high-accuracy real-time dense SLAM, even with high-parallelism architecture such as GPUs. To tackle this problem, one solution is to design proper approximation techniques for dense SLAM on embedded GPUs. In this work, we propose two novel approximation techniques, critical data identification and redundant branch elimination. We also analyze the error characteristics of the other two techniques—loop skipping and thread approximation. Then, we propose SLaPP, an online adaptive approximation controller, which aims to control the error to be under an acceptable threshold. The evaluation shows SLaPP can achieve 2.0× performance speedup and 30% energy saving on average compared to the case without approximation.


2022 ◽  
Vol 8 ◽  
pp. 1602-1633
Author(s):  
Da-sheng Lee ◽  
Yan-Tang Chen ◽  
Shih-Lung Chao

2022 ◽  
Vol 51 ◽  
pp. 101911
Author(s):  
Lina Saldukaitė ◽  
Egidijus Šarauskis ◽  
Andrii Zabrodskyi ◽  
Aida Adamavičienė ◽  
Sidona Buragienė ◽  
...  

2022 ◽  
Vol 156 ◽  
pp. 111992
Author(s):  
Xiu'e Yang ◽  
Shuli Liu ◽  
Yuliang Zou ◽  
Wenjie Ji ◽  
Qunli Zhang ◽  
...  

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