853 research outputs found
JALAD: Joint Accuracy- and Latency-Aware Deep Structure Decoupling for Edge-Cloud Execution
Recent years have witnessed a rapid growth of deep-network based services and
applications. A practical and critical problem thus has emerged: how to
effectively deploy the deep neural network models such that they can be
executed efficiently. Conventional cloud-based approaches usually run the deep
models in data center servers, causing large latency because a significant
amount of data has to be transferred from the edge of network to the data
center. In this paper, we propose JALAD, a joint accuracy- and latency-aware
execution framework, which decouples a deep neural network so that a part of it
will run at edge devices and the other part inside the conventional cloud,
while only a minimum amount of data has to be transferred between them. Though
the idea seems straightforward, we are facing challenges including i) how to
find the best partition of a deep structure; ii) how to deploy the component at
an edge device that only has limited computation power; and iii) how to
minimize the overall execution latency. Our answers to these questions are a
set of strategies in JALAD, including 1) A normalization based in-layer data
compression strategy by jointly considering compression rate and model
accuracy; 2) A latency-aware deep decoupling strategy to minimize the overall
execution latency; and 3) An edge-cloud structure adaptation strategy that
dynamically changes the decoupling for different network conditions.
Experiments demonstrate that our solution can significantly reduce the
execution latency: it speeds up the overall inference execution with a
guaranteed model accuracy loss.Comment: conference, copyright transfered to IEE
Curriculum Graph Machine Learning: A Survey
Graph machine learning has been extensively studied in both academia and
industry. However, in the literature, most existing graph machine learning
models are designed to conduct training with data samples in a random order,
which may suffer from suboptimal performance due to ignoring the importance of
different graph data samples and their training orders for the model
optimization status. To tackle this critical problem, curriculum graph machine
learning (Graph CL), which integrates the strength of graph machine learning
and curriculum learning, arises and attracts an increasing amount of attention
from the research community. Therefore, in this paper, we comprehensively
overview approaches on Graph CL and present a detailed survey of recent
advances in this direction. Specifically, we first discuss the key challenges
of Graph CL and provide its formal problem definition. Then, we categorize and
summarize existing methods into three classes based on three kinds of graph
machine learning tasks, i.e., node-level, link-level, and graph-level tasks.
Finally, we share our thoughts on future research directions. To the best of
our knowledge, this paper is the first survey for curriculum graph machine
learning.Comment: IJCAI 2023 Survey Trac
Research on SLM Algorithm for PAPR reduction in MB-OFDM UWB Systems
AbstractMultiband orthogonal frequency division multiplexing (MB-OFDM) is one of the key techniques of ultra wideband (UWB) systems. A major drawback of MB-OFDM technique is the high peak-to-average power ratio (PAPR) of the transmit signal. In this paper, a novel phase sequence of selected mapping algorithm which makes the side information not needed is designed to lower the PAPR of MB-OFDM UWB signals. It is also shown that comparable PAPR reduction performance with the original SLM algorithm can be achieved with a small increase in signal power. Simulation results show that there must be equilibriums between SLM computational complexity and PAPR performance. The objective of the new algorithm is to lower PAPR close to ordinary SLM technique with reduced computational complexity with little performance degradation and achieves better system resource utilization
- …