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Manni Wang

dblp:205/6821 · DBLP profile ↗
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2ranked-venue papers
1as first author
2since 2021 · last 2022
0000-0002-3655-6110ORCID · corroborated

Domains — the database's venue-derived domains; a paper can count in several

Computer networks · 2 · 1 first-author · 2 since 2021

Expertise — from the expertise taxonomy: the topics of the expert's papers under the CCF categories. A weight counts papers with recency: 1 for a paper about the topic, 0.3 when the topic is its context, halved every five years.

Computer architecture, parallel and distributed computing, and storage systems
2 papers
Processor architecture and microarchitecture · 61% GPUs and heterogeneous computing · 21% Energy-efficient computing · 18%
Software engineering, system software, and programming languages
1 paper
Compilers and program optimization · 100%
Artificial intelligence
1 paper
Efficient and distributed learning · 100%

Topics — the 5 heaviest of 5, each with the papers that count most for it

TopicWeightPapersLastEvidence papers
Compilers and program optimization
code generation		 0.612022
Romou: rapidly generate high-performance tensor kernels for mobile GPUs · MobiCom 2022
Machine learning › Efficient and distributed learning
on-device inference		 0.512021
AsyMo: scalable and efficient deep-learning inference on asymmetric mobile CPUs · MobiCom 2021
Processor architecture and microarchitecture › multiprocessor architecture
asymmetric multiprocessor		 0.512021
AsyMo: scalable and efficient deep-learning inference on asymmetric mobile CPUs · MobiCom 2021
GPUs and heterogeneous computing › embedded GPU
mobile GPU		 0.212022
Romou: rapidly generate high-performance tensor kernels for mobile GPUs · MobiCom 2022
Energy-efficient computing › energy-efficient machine learning
energy-efficient inference		 0.112021
AsyMo: scalable and efficient deep-learning inference on asymmetric mobile CPUs · MobiCom 2021

Methods — techniques the papers use, named apart from their topics

automatic kernel generation · 1.1deep learning inference scheduling · 1.0
YearPublicationVenuePosition
2022 Romou: rapidly generate high-performance tensor kernels for mobile GPUs
abstract
Mobile GPU, as a ubiquitous and powerful accelerator, plays an important role in accelerating on-device DNN (Deep Neural Network) inference. The frequent-upgrade and diversity of mobile GPUs require automatic kernel generation to empower fast DNN deployment. However, current generated kernels have poor performance.
Rendong Liang, Ting Cao 0003, Jicheng Wen, Manni Wang, Yang Wang 0053, Jianhua Zou, Yunxin Liu 0001
 MobiCom4
2021 AsyMo: scalable and efficient deep-learning inference on asymmetric mobile CPUs
abstract
On-device deep learning (DL) inference has attracted vast interest. Mobile CPUs are the most common hardware for on-device inference and many inference frameworks have been developed for them. Yet, due to the hardware complexity, DL inference on mobile CPUs suffers from two common issues: the poor performance scalability on the asymmetric multiprocessor, and energy inefficiency.
Manni Wang, Shaohua Ding, Ting Cao 0003, Yunxin Liu 0001, Fengyuan Xu
 MobiCom1