12,462 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
Topological structures in the equities market network
We present a new method for articulating scale-dependent topological
descriptions of the network structure inherent in many complex systems. The
technique is based on "Partition Decoupled Null Models,'' a new class of null
models that incorporate the interaction of clustered partitions into a random
model and generalize the Gaussian ensemble. As an application we analyze a
correlation matrix derived from four years of close prices of equities in the
NYSE and NASDAQ. In this example we expose (1) a natural structure composed of
two interacting partitions of the market that both agrees with and generalizes
standard notions of scale (eg., sector and industry) and (2) structure in the
first partition that is a topological manifestation of a well-known pattern of
capital flow called "sector rotation.'' Our approach gives rise to a natural
form of multiresolution analysis of the underlying time series that naturally
decomposes the basic data in terms of the effects of the different scales at
which it clusters. The equities market is a prototypical complex system and we
expect that our approach will be of use in understanding a broad class of
complex systems in which correlation structures are resident.Comment: 17 pages, 4 figures, 3 table
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