149 research outputs found
Proposal of Automatic Offloading Method in Mixed Offloading Destination Environment
When using heterogeneous hardware, barriers of technical skills such as
OpenMP, CUDA and OpenCL are high. Based on that, I have proposed
environment-adaptive software that enables automatic conversion, configuration.
However, including existing technologies, there has been no research to
properly and automatically offload the mixed offloading destination environment
such as GPU, FPGA and many core CPU. In this paper, as a new element of
environment-adaptive software, I study a method for offloading applications
properly and automatically in the environment where the offloading destination
is mixed with GPU, FPGA and many core CPU.Comment: 5 pages, 3 figure
Proposal of Automatic Offloading for Function Blocks of Applications
When using heterogeneous hardware other than CPUs, barriers of technical
skills such as OpenCL are high. Based on that, I have proposed environment
adaptive software that enables automatic conversion, configuration, and
high-performance operation of once written code, according to the hardware to
be placed. Partly of the offloading to the GPU was automated previously. In
this paper, I propose and evaluate an automatic extraction method of
appropriate offload target loop statements of source code as the first step of
offloading to FPGA. I evaluate the effectiveness of the proposed method in
multiple applications.Comment: 8 pages, 5 figures, The 8th IIAE International Conference on
Industrial Application Engineering 2020 (ICIAE 2020), pp.4-11, Mar. 202
Proposal of Automatic FPGA Offloading for Applications Loop Statements
In recent years, with the prediction of Moore's law slowing down, utilization
of hardware other than CPU such as FPGA which is energy effective is
increasing. However, when using heterogeneous hardware other than CPUs,
barriers of technical skills such as OpenCL are high. Based on that, I have
proposed environment adaptive software that enables automatic conversion,
configuration, and high-performance operation of once written code, according
to the hardware to be placed. Partly of the offloading to the GPU was automated
previously. In this paper, I propose and evaluate an automatic extraction
method of appropriate offload target loop statements of source code as the
first step of offloading to FPGA. I evaluate the effectiveness of the proposed
method using existing applications.Comment: 13 pages, 4 figures, The 7th Annual Conference on Engineering and
Information Technology (ACEAIT 2020), pp.111-123, 202
Evaluation of Automatic GPU and FPGA Offloading for Function Blocks of Applications
In the recent years, systems using FPGAs, GPUs have increased due to their
advantages such as power efficiency compared to CPUs. However, use in systems
such as FPGAs and GPUs requires understanding hardware-specific technical
specifications such as HDL and CUDA, which is a high hurdle. Based on this
background, I previously proposed environment adaptive software that enables
automatic conversion, configuration, and high-performance operation of once
written code according to the hardware to be placed. As an element of the
concept, I proposed a method to automatically offload loop statements of
application source code for CPU to FPGA and GPU. In this paper, I propose and
evaluate a method for offloading a function block, which is a larger unit,
instead of individual loop statements in an application, to achieve higher
speed by automatic offloading to GPU and FPGA. I implement the proposed method
and evaluate with existing applications offloading to GPU.Comment: 8 pages, 5 figures, in Japanese, IEICE Technical Report, SC2019-4
Study of Automatic GPU Offloading Method from Various Language Applications
In recent years, utilization of heterogeneous hardware other than small core
CPU such as GPU, FPGA or many core CPU is increasing. However, when using
heterogeneous hardware, barriers of technical skills such as CUDA are high.
Based on that, I have proposed environment-adaptive software that enables
automatic conversion, configuration, and high performance operation of once
written code, according to the hardware to be placed. However, the source
language for offloading was mainly C/C++ language applications currently, and
there was no research for common offloading for various language applications.
In this paper, I study a common method for automatically offloading for various
language applications not only in C language but also in Python and Java.Comment: 6 pages, 1 figure, in Japanes
Study of Resource Amount Configuration for Automatic Application Offloading
In recent years, utilization of heterogeneous hardware other than small core
CPU such as GPU, FPGA or many core CPU is increasing. However, when using
heterogeneous hardware, barriers of technical skills such as OpenMP, CUDA and
OpenCL are high. Based on that, I have proposed environment-adaptive software
that enables automatic conversion, configuration, and high performance
operation of once written code, according to the hardware to be placed.
However, although the conversion of the code according to the migration
destination environment has been studied so far, there has been no research to
properly set the resource amount. In this paper, as a new element of
environment adaptive software, in order to operate the application with high
cost performance, I study a method to optimize the resource amount of CPUs and
offload devices.Comment: 6 pages, 1 figure, in Japanes
Edge Intelligence: Architectures, Challenges, and Applications
Edge intelligence refers to a set of connected systems and devices for data
collection, caching, processing, and analysis in locations close to where data
is captured based on artificial intelligence. The aim of edge intelligence is
to enhance the quality and speed of data processing and protect the privacy and
security of the data. Although recently emerged, spanning the period from 2011
to now, this field of research has shown explosive growth over the past five
years. In this paper, we present a thorough and comprehensive survey on the
literature surrounding edge intelligence. We first identify four fundamental
components of edge intelligence, namely edge caching, edge training, edge
inference, and edge offloading, based on theoretical and practical results
pertaining to proposed and deployed systems. We then aim for a systematic
classification of the state of the solutions by examining research results and
observations for each of the four components and present a taxonomy that
includes practical problems, adopted techniques, and application goals. For
each category, we elaborate, compare and analyse the literature from the
perspectives of adopted techniques, objectives, performance, advantages and
drawbacks, etc. This survey article provides a comprehensive introduction to
edge intelligence and its application areas. In addition, we summarise the
development of the emerging research field and the current state-of-the-art and
discuss the important open issues and possible theoretical and technical
solutions.Comment: 53 pages, 37 figures, surve
Improvement of Automatic GPU Offloading Technology for Application Loop Statements
In recent years, with the slowing down of Moore's law, utilization of
hardware other than CPU such as GPU or FPGA is increasing. However, when using
heterogeneous hardware other than CPUs, barriers of technical skills such as
CUDA and HDL are high. Based on that, I have proposed environment adaptive
software that enables automatic conversion, configuration, and high-performance
operation of once written code, according to the hardware to be placed. Partly
of the offloading to the GPU and FPGA was automated previously. In this paper,
I improve and propose a previous automatic GPU offloading method to expand
applicapable software and enhance performances more. I evaluate the
effectiveness of the proposed method in multiple applications.Comment: 7 pages, 5 figure, in Japanese, IEICE Technical Report, NS2019-21
Convergence of Edge Computing and Deep Learning: A Comprehensive Survey
Ubiquitous sensors and smart devices from factories and communities are
generating massive amounts of data, and ever-increasing computing power is
driving the core of computation and services from the cloud to the edge of the
network. As an important enabler broadly changing people's lives, from face
recognition to ambitious smart factories and cities, developments of artificial
intelligence (especially deep learning, DL) based applications and services are
thriving. However, due to efficiency and latency issues, the current cloud
computing service architecture hinders the vision of "providing artificial
intelligence for every person and every organization at everywhere". Thus,
unleashing DL services using resources at the network edge near the data
sources has emerged as a desirable solution. Therefore, edge intelligence,
aiming to facilitate the deployment of DL services by edge computing, has
received significant attention. In addition, DL, as the representative
technique of artificial intelligence, can be integrated into edge computing
frameworks to build intelligent edge for dynamic, adaptive edge maintenance and
management. With regard to mutually beneficial edge intelligence and
intelligent edge, this paper introduces and discusses: 1) the application
scenarios of both; 2) the practical implementation methods and enabling
technologies, namely DL training and inference in the customized edge computing
framework; 3) challenges and future trends of more pervasive and fine-grained
intelligence. We believe that by consolidating information scattered across the
communication, networking, and DL areas, this survey can help readers to
understand the connections between enabling technologies while promoting
further discussions on the fusion of edge intelligence and intelligent edge,
i.e., Edge DL.Comment: To be published in IEEE Communications Surveys and Tutorial
Machine Learning Systems for Intelligent Services in the IoT: A Survey
Machine learning (ML) technologies are emerging in the Internet of Things
(IoT) to provision intelligent services. This survey moves beyond existing ML
algorithms and cloud-driven design to investigate the less-explored systems,
scaling and socio-technical aspects for consolidating ML and IoT. It covers the
latest developments (up to 2020) on scaling and distributing ML across cloud,
edge, and IoT devices. With a multi-layered framework to classify and
illuminate system design choices, this survey exposes fundamental concerns of
developing and deploying ML systems in the rising cloud-edge-device continuum
in terms of functionality, stakeholder alignment and trustworthiness.Comment: Requires rewor
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