Heterogeneous Sensor Data Analysis Using Efficient Adaptive Artificial Neural Network on FPGA Based Edge Gateway

Gaikwad, Nikhil B and Tiwari, Varun and Keskar, Avinash and Shivaprakash, N C (2019) Heterogeneous Sensor Data Analysis Using Efficient Adaptive Artificial Neural Network on FPGA Based Edge Gateway. In: KSII TRANSACTIONS ON INTERNET AND INFORMATION SYSTEMS, 13 (10). pp. 4865-4885.

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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.

Item Type:	Journal Article
Publication:	KSII TRANSACTIONS ON INTERNET AND INFORMATION SYSTEMS
Publisher:	KSII-KOR SOC INTERNET INFORMATION
Additional Information:	Copyright of this article belongs to KSII-KOR SOC INTERNET INFORMATION
Keywords:	Real-time data analysis; field programmable gate array; adaptive artificial neural network; edge gateway; fog computing; smart wearables
Department/Centre:	Division of Physical & Mathematical Sciences > Instrumentation Appiled Physics
Date Deposited:	10 Dec 2019 10:14
Last Modified:	10 Dec 2019 10:14
URI:	http://eprints.iisc.ac.in/id/eprint/63955

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