A comprehensive benchmark of deep learning libraries on mobile devices
Deploying deep learning (DL) on mobile devices has been a notable trend in recent years.
To support fast inference of on-device DL, DL libraries play a critical role as algorithms and …
To support fast inference of on-device DL, DL libraries play a critical role as algorithms and …
A comprehensive deep learning library benchmark and optimal library selection
Deploying deep learning (DL) on mobile devices has been a notable trend in recent years.
To support fast inference of on-device DL, DL libraries play a critical role as algorithms and …
To support fast inference of on-device DL, DL libraries play a critical role as algorithms and …
A survey of deep learning on mobile devices: Applications, optimizations, challenges, and research opportunities
Deep learning (DL) has demonstrated great performance in various applications on
powerful computers and servers. Recently, with the advancement of more powerful mobile …
powerful computers and servers. Recently, with the advancement of more powerful mobile …
MNN: A universal and efficient inference engine
X Jiang, H Wang, Y Chen, Z Wu… - Proceedings of …, 2020 - proceedings.mlsys.org
Deploying deep learning (DL) models on mobile devices draws more and more attention
recently. However, designing an efficient inference engine on devices is under the great …
recently. However, designing an efficient inference engine on devices is under the great …
Custom hardware architectures for deep learning on portable devices: a review
The staggering innovations and emergence of numerous deep learning (DL) applications
have forced researchers to reconsider hardware architecture to accommodate fast and …
have forced researchers to reconsider hardware architecture to accommodate fast and …
Mdinference: Balancing inference accuracy and latency for mobile applications
Deep Neural Networks are allowing mobile devices to incorporate a wide range of features
into user applications. However, the computational complexity of these models makes it …
into user applications. However, the computational complexity of these models makes it …
An empirical study towards characterizing deep learning development and deployment across different frameworks and platforms
Deep Learning (DL) has recently achieved tremendous success. A variety of DL frameworks
and platforms play a key role to catalyze such progress. However, the differences in …
and platforms play a key role to catalyze such progress. However, the differences in …
[HTML][HTML] Efficient deep learning inference on edge devices
Deploying deep learning (DL) models on edge devices is getting popular nowadays. The
huge diversity of edge devices, with both computation and memory constraints, however …
huge diversity of edge devices, with both computation and memory constraints, however …
Deep learning on computational‐resource‐limited platforms: A survey
C Chen, P Zhang, H Zhang, J Dai, Y Yi… - Mobile Information …, 2020 - Wiley Online Library
Nowadays, Internet of Things (IoT) gives rise to a huge amount of data. IoT nodes equipped
with smart sensors can immediately extract meaningful knowledge from the data through …
with smart sensors can immediately extract meaningful knowledge from the data through …
Benchmarking deep learning frameworks: Design considerations, metrics and beyond
With increasing number of open-source deep learning (DL) software tools made available,
benchmarking DL software frameworks and systems is in high demand. This paper presents …
benchmarking DL software frameworks and systems is in high demand. This paper presents …