2,071 research outputs found
Analyzing the Hardware-Software Implications of Multi-modal DNN Workloads using MMBench
The explosive growth of various types of big data and advances in AI
technologies have catalyzed a new type of applications called multi-modal DNNs.
Multi-modal DNNs are capable of interpreting and reasoning about information
from multiple modalities, making them more applicable to real-world AI
scenarios. In recent research, multi-modal DNNs have outperformed the best
uni-modal DNN in a wide range of applications from traditional multimedia to
emerging autonomous systems. However, despite their importance and superiority,
very limited research attention has been devoted to understand the
characteristics of multi-modal DNNs and their implications on current computing
software/hardware platforms.
To facilitate research and advance the understanding of these multi-modal DNN
workloads, we first present MMbench, an open-source benchmark suite consisting
of a set of real-world multi-modal DNN workloads with relevant performance
metrics for evaluation. Then we use MMbench to conduct an in-depth analysis on
the characteristics of multi-modal DNNs. We study their implications on
application and programming framework, operating and scheduling system, as well
as execution hardware. Finally, we conduct a case study and extend our
benchmark to edge devices. We hope that our work can provide guidance for
future software/hardware design and optimization to underpin multi-modal DNNs
on both cloud and edge computing platforms
SciTech News Volume 71, No. 1 (2017)
Columns and Reports From the Editor 3
Division News Science-Technology Division 5 Chemistry Division 8 Engineering Division Aerospace Section of the Engineering Division 9 Architecture, Building Engineering, Construction and Design Section of the Engineering Division 11
Reviews Sci-Tech Book News Reviews 12
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Big Data Platform Architecture Under The Background of Financial Technology
With the rise of the concept of financial technology, financial and
technology gradually in-depth integration, scientific and technological means
to become financial product innovation, improve financial efficiency and reduce
financial transaction costs an important driving force. In this context, the
new technology platform is from the business philosophy, business model,
technical means, sales, internal management, and other dimensions to re-shape
the financial industry. In this paper, the existing big data platform
architecture technology innovation, adding space-time data elements, combined
with the insurance industry for practical analysis, put forward a meaningful
product circle and customer circle.Comment: 4 pages, 3 figures, 2018 International Conference on Big Data
Engineering and Technolog
Distributed Sensing, Computing, Communication, and Control Fabric: A Unified Service-Level Architecture for 6G
With the advent of the multimodal immersive communication system, people can
interact with each other using multiple devices for sensing, communication
and/or control either onsite or remotely. As a breakthrough concept, a
distributed sensing, computing, communications, and control (DS3C) fabric is
introduced in this paper for provisioning 6G services in multi-tenant
environments in a unified manner. The DS3C fabric can be further enhanced by
natively incorporating intelligent algorithms for network automation and
managing networking, computing, and sensing resources efficiently to serve
vertical use cases with extreme and/or conflicting requirements. As such, the
paper proposes a novel end-to-end 6G system architecture with enhanced
intelligence spanning across different network, computing, and business
domains, identifies vertical use cases and presents an overview of the relevant
standardization and pre-standardization landscape
Deep neural networks in the cloud: Review, applications, challenges and research directions
Deep neural networks (DNNs) are currently being deployed as machine learning technology in a wide
range of important real-world applications. DNNs consist of a huge number of parameters that require
millions of floating-point operations (FLOPs) to be executed both in learning and prediction modes. A
more effective method is to implement DNNs in a cloud computing system equipped with centralized
servers and data storage sub-systems with high-speed and high-performance computing capabilities.
This paper presents an up-to-date survey on current state-of-the-art deployed DNNs for cloud computing.
Various DNN complexities associated with different architectures are presented and discussed alongside
the necessities of using cloud computing. We also present an extensive overview of different cloud
computing platforms for the deployment of DNNs and discuss them in detail. Moreover, DNN applications
already deployed in cloud computing systems are reviewed to demonstrate the advantages of using
cloud computing for DNNs. The paper emphasizes the challenges of deploying DNNs in cloud computing
systems and provides guidance on enhancing current and new deployments.The EGIA project (KK-2022/00119The
Consolidated Research Group MATHMODE (IT1456-22
Supporting UAVs with Edge Computing: A Review of Opportunities and Challenges
Over the last years, Unmanned Aerial Vehicles (UAVs) have seen significant
advancements in sensor capabilities and computational abilities, allowing for
efficient autonomous navigation and visual tracking applications. However, the
demand for computationally complex tasks has increased faster than advances in
battery technology. This opens up possibilities for improvements using edge
computing. In edge computing, edge servers can achieve lower latency responses
compared to traditional cloud servers through strategic geographic deployments.
Furthermore, these servers can maintain superior computational performance
compared to UAVs, as they are not limited by battery constraints. Combining
these technologies by aiding UAVs with edge servers, research finds measurable
improvements in task completion speed, energy efficiency, and reliability
across multiple applications and industries. This systematic literature review
aims to analyze the current state of research and collect, select, and extract
the key areas where UAV activities can be supported and improved through edge
computing
Vehicle as a Service (VaaS): Leverage Vehicles to Build Service Networks and Capabilities for Smart Cities
Smart cities demand resources for rich immersive sensing, ubiquitous
communications, powerful computing, large storage, and high intelligence
(SCCSI) to support various kinds of applications, such as public safety,
connected and autonomous driving, smart and connected health, and smart living.
At the same time, it is widely recognized that vehicles such as autonomous
cars, equipped with significantly powerful SCCSI capabilities, will become
ubiquitous in future smart cities. By observing the convergence of these two
trends, this article advocates the use of vehicles to build a cost-effective
service network, called the Vehicle as a Service (VaaS) paradigm, where
vehicles empowered with SCCSI capability form a web of mobile servers and
communicators to provide SCCSI services in smart cities. Towards this
direction, we first examine the potential use cases in smart cities and
possible upgrades required for the transition from traditional vehicular ad hoc
networks (VANETs) to VaaS. Then, we will introduce the system architecture of
the VaaS paradigm and discuss how it can provide SCCSI services in future smart
cities, respectively. At last, we identify the open problems of this paradigm
and future research directions, including architectural design, service
provisioning, incentive design, and security & privacy. We expect that this
paper paves the way towards developing a cost-effective and sustainable
approach for building smart cities.Comment: 32 pages, 11 figure
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