scholarly journals Study of Automatic FPGA Offloading for Loop Statements of Applications

2021 ◽  
Author(s):  
Yoji Yamato
Keyword(s):  
High Performance ◽  
Technical Skills ◽  
Application Software ◽  
Adaptive Software ◽  
Heterogeneous Hardware ◽  
Iot Devices ◽  
Network Software

IEICE technical workshop on Network Software, NWS-19-6.Recently, heterogeneous hardware such as GPU and FPGA is used in many systems and also IoT devices are increased rapidly. However, to utilize heterogeneous hardware, the hurdles are high because of much technical skills. I have proposed environment adaptive software to operate an once written application with high performance by automatically converting the code and configuring setting so that we can utilize GPU, FPGA and IoT devices in the location to be deployed and I have also achieved automatic GPU offloading partly. In this paper, I study a method of FPGA offloading which automatically extracts appropriate loop statements of application software.

scholarly journals Study of Automatic Offloading Method in Mixed Offloading Destination Environment 移行先混在環境における自動オフロード方式の検討

2021 ◽  
Author(s):  
Yoji Yamato
Keyword(s):  
High Performance ◽  
Technical Skills ◽  
Adaptive Software ◽  
Small Core ◽  
Technical Report ◽  
Heterogeneous Hardware ◽  
Many Core

IEICE Technical Report, IN2020-30.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, 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. I evaluate the effectiveness of the proposed method in multiple applications.

scholarly journals Initial study of environment-adaptive application placement optimization

2021 ◽  
Author(s):  
Yoji Yamato
Keyword(s):  
Response Time ◽  
High Performance ◽  
Initial Study ◽  
Adaptive Software ◽  
Placement Method ◽  
Placement Optimization ◽  
Application Placement ◽  
The Cost ◽  
Network Software

IEICE Technical Workshop on Network Software, BS-25-2.I have proposed environment adaptive software that automatically converts, resource settings, and deploys the code once written according to the deployment destination environment hardware, and operates it with high performance. I also have been working on automatic conversion of code to GPU and FPGA. In this paper, I initially examine an appropriate placement method that satisfies the user's requirements and reduces the cost, response time and so on of an application that has been automatically converted so that it can be placed on GPU or FPGA.

scholarly journals Study of appropriate offload destination based on software arithmetic intensity

2021 ◽  
Author(s):  
Yoji Yamato
Keyword(s):  
High Performance ◽  
Adaptive Software ◽  
Long Time ◽  
Network Software

IEICE Technical Workshop on Network Software, BS-26-2.I have proposed environment-adaptive software that automatically converts the description code according to the destination device and operates it with high performance. Currently, for environments where GPUs, FPGAs, and manycore CPUs are mixed in the cloud or so on, all offload destination performance is verified and the fastest migration destination is selected, and it takes a long time. In this paper, I use the Arithmetic Intensity of the application loop statement to verify whether it is possible to select for GPU or manycore CPU.

scholarly journals Machine Learning-Based IoT-Botnet Attack Detection with Sequential Architecture

Sensors ◽  
2020 ◽  
Vol 20 (16) ◽  
pp. 4372 ◽  
Author(s):  
Yan Naung Soe ◽  
Yaokai Feng ◽  
Paulus Insap Santosa ◽  
Rudy Hartanto ◽  
Kouichi Sakurai
Keyword(s):  
Machine Learning ◽  
High Performance ◽  
Detection System ◽  
Rapid Development ◽  
Attack Detection ◽  
Cyber Attacks ◽  
Detection Systems ◽  
Iot Devices ◽  
Artificial Neural Network Ann ◽  
Feature Selection Approach

With the rapid development and popularization of Internet of Things (IoT) devices, an increasing number of cyber-attacks are targeting such devices. It was said that most of the attacks in IoT environments are botnet-based attacks. Many security weaknesses still exist on the IoT devices because most of them have not enough memory and computational resource for robust security mechanisms. Moreover, many existing rule-based detection systems can be circumvented by attackers. In this study, we proposed a machine learning (ML)-based botnet attack detection framework with sequential detection architecture. An efficient feature selection approach is adopted to implement a lightweight detection system with a high performance. The overall detection performance achieves around 99% for the botnet attack detection using three different ML algorithms, including artificial neural network (ANN), J48 decision tree, and Naïve Bayes. The experiment result indicates that the proposed architecture can effectively detect botnet-based attacks, and also can be extended with corresponding sub-engines for new kinds of attacks.

Improved Usability of IOT Devices in Healthcare Using Big Data Analysis

2019 ◽  
pp. 110-127
Author(s):  
Vijayalakshmi Kakulapati ◽  
Mahender Reddy S.
Keyword(s):  
Medical Devices ◽  
High Performance ◽  
Big Data Analysis ◽  
Sensor Data ◽  
Sensor Output ◽  
Human Intervention ◽  
Patient Health Status ◽  
Alert Message ◽  
Patient Health ◽  
Iot Devices

Sensor data takes the microcontroller and sends it to doctors through the wi-fi network and provides real-time healthcare parameter monitoring. The clinician can analyze the sensor generated information. Patients provide their measures to the arrangement and identify their fitness status without human intervention. In this chapter, MapReduce algorithm is used to identify the patient health status. The controller is connected with the signal to alert the attendee about dissimilarity in sensor output data. If the situation is sever, an alert message is sent to the doctor through the IOT devices that can provide quick provisional medication to the ill person. The system improves usability of medical devices with less power consumption, simple setup, and high performance and response.

scholarly journals Design and Evaluation of a New Machine Learning Framework for IoT and Embedded Devices

Electronics ◽  
2021 ◽  
Vol 10 (5) ◽  
pp. 600
Author(s):  
Gianluca Cornetta ◽  
Abdellah Touhafi
Keyword(s):  
Machine Learning ◽  
Data Analysis ◽  
High Performance ◽  
Low Cost ◽  
Learning Algorithms ◽  
Machine Learning Algorithms ◽  
Smart Devices ◽  
Raspberry Pi ◽  
Embedded Devices ◽  
Iot Devices

Low-cost, high-performance embedded devices are proliferating and a plethora of new platforms are available on the market. Some of them either have embedded GPUs or the possibility to be connected to external Machine Learning (ML) algorithm hardware accelerators. These enhanced hardware features enable new applications in which AI-powered smart objects can effectively and pervasively run in real-time distributed ML algorithms, shifting part of the raw data analysis and processing from cloud or edge to the device itself. In such context, Artificial Intelligence (AI) can be considered as the backbone of the next generation of Internet of the Things (IoT) devices, which will no longer merely be data collectors and forwarders, but really “smart” devices with built-in data wrangling and data analysis features that leverage lightweight machine learning algorithms to make autonomous decisions on the field. This work thoroughly reviews and analyses the most popular ML algorithms, with particular emphasis on those that are more suitable to run on resource-constrained embedded devices. In addition, several machine learning algorithms have been built on top of a custom multi-dimensional array library. The designed framework has been evaluated and its performance stressed on Raspberry Pi III- and IV-embedded computers.

scholarly journals High Performance Predictive Analytics in IoT

2019 ◽  
Vol 8 (4) ◽  
pp. 9266-9270
Keyword(s):  
Real Time ◽  
Network Architecture ◽  
High Performance ◽  
Predictive Analytics ◽  
Time Data ◽  
New Techniques ◽  
Real Time Data ◽  
Iot Devices ◽  
Indexing Technique ◽  
To Receive

Internet of things (IoT) is a quick-moving gathering of web associated sensors implanted in a wide-extending assortment of physical articles. While things can be any physical item (energize or lifeless) on the planet, to which you could associate or implant a sensor. Sensors can take countless potential estimations. Sensors produce gigantic measures of new, organized, unstructured, ongoing information, and structures enormous information. IoT information is exceptionally huge and confused, which can give genuine-time setting and supposition data about genuine articles or nature. Among the different challenges that the present IoT is facing, the three prime areas of concern are, need of efficient framework to receive IoT data, a need of a new scalable parallel indexing technique for efficiently storing IoT data and securing IoT generated data at all the stages i.e. from the edge devices to the cloud. A new efficient framework is introduced, which can retrieve meaningful information from these IoT devices and efficiently index it. For processing such enormous real time data generated from IoT devices, new techniques are introducing which are scalable and secure. The research proposes a general IoT network architecture. It describes the interconnectivity among the different things such as sensors, receivers and cloud. The proposed architecture efficiently receives real time data from all the sensors. The prime focus is on the elimination of the existing issues in IoT. Along with this, the provision has to make for standard future proofing against these new proposed schemes.

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