Title
Heterogeneous Sensor Data Analysis Using Efficient Adaptive Artificial Neural Network on FPGA Based Edge Gateway.
Abstract
We propose a FPGA based design that performs real-time power-efficient analysis of heterogeneous sensor data using adaptive ANN on edge gateway of smart military wearables. In this work, four independent ANN classifiers are developed with optimum topologies. Out of which human activity, BP and toxic gas classifier are multiclass and ECG classifier is binary. These classifiers are later integrated into a single adaptive ANN hardware with a select line( s) that switches the hardware architecture as per the sensor type. Five versions of adaptive ANN with different precisions have been synthesized into IP cores. These IP cores are implemented and tested on Xilinx Artix-7 FPGA using Microblaze test system and LabVIEW based sensor simulators. The hardware analysis shows that the adaptive ANN even with 8-bit precision is the most efficient IP core in terms of hardware resource utilization and power consumption without compromising much on classification accuracy. This IP core requires only 31 microseconds for classification by consuming only 12 milliwatts of power. The proposed adaptive ANN design saves 61% to 97% of different FPGA resources and 44% of power as compared with the independent implementations. In addition, 96.87% to 98.75% of data throughput reduction is achieved by this edge gateway.
Year
DOI
Venue
2019
10.3837/tiis.2019.10.003
KSII TRANSACTIONS ON INTERNET AND INFORMATION SYSTEMS
Keywords
Field
DocType
Real-time data analysis,field programmable gate array,adaptive artificial neural network,edge gateway,fog computing,smart wearables
Computer architecture,Computer science,Computer network,Field-programmable gate array,Default gateway,Artificial neural network
Journal
Volume
Issue
ISSN
13
10
1976-7277
Citations 
PageRank 
References 
0
0.34
0
Authors
4
Name
Order
Citations
PageRank
Nikhil B. Gaikwad101.35
Varun Tiwari262.13
Avinash G. Keskar3127.41
N. C. Shivaprakash441.40