TIIS (한국인터넷정보학회)
Current Result Document :
| 한글제목(Korean Title) |
Heterogeneous Sensor Data Analysis Using Efficient Adaptive Artificial Neural Network on FPGA Based Edge Gateway |
| 영문제목(English Title) |
Heterogeneous Sensor Data Analysis Using Efficient Adaptive Artificial Neural Network on FPGA Based Edge Gateway |
| 저자(Author) |
Nikhil B. Gaikwad
Varun Tiwari
Avinash Keskar
NC Shivaprakash
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| 원문수록처(Citation) |
VOL 13 NO. 10 PP. 4865 ~ 4885 (2019. 10) |
한글내용
(Korean Abstract) |
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영문내용
(English 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.
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| 키워드(Keyword) |
Real-time data analysis
field programmable gate array
adaptive artificial neural network
edge gateway
fog computing
smart wearables
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| 파일첨부 |
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