도플러 레이다 및 음성 센서를 활용한CNN 기반 HMI 시스템 설계 및 구현
Design and Implementation of CNN-based HMI System using Doppler Radar and Voice Sensor
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초록
본 논문에서는 도플러 레이다와 음성 센서를 이용한 CNN 기반 HMI 시스템을 제안하고, 가속을 위한 하드웨어 설계 및구현 결과를 제시한다. 단일 센서 모니터링의 한계를 극복하기 위해 제안된 HMI 시스템은 두 센서의 데이터를 융합 처리하여 분류 성능을 개선했다. 제안된 시스템은 다양한 노이즈 환경에서 단일 레이다 및 음성 센서 기반 분류기에 비해 3.5% 및12% 향상된 성능을 나타냈다. 또한, CNN의 복잡한 연산부를 가속하기 위해 설계된 하드웨어를 FPGA 디바이스 상에서 구현 및 검증하였다. 성능 평가 결과, 제안된 HMI 가속 플랫폼은 단일 소프트웨어 기반 구조에 비해 연산 시간을 95% 단축가능한 것을 확인하였다.
Abstract
In this paper, we propose CNN-based HMI system using Doppler radar and voice sensor, and present hardware designand implementation results. To overcome the limitation of single sensor monitoring, the proposed HMI system combinesdata from two sensors to improve performance. The proposed system exhibits improved performance by 3.5% and 12%compared to a single radar and voice sensor-based classifier in noisy environment. In addition, hardware to accelerate thecomplex computational unit of CNN is implemented and verified on the FPGA test system. As a result of performanceevaluation, the proposed HMI acceleration platform can be processed with 95% reduction in computation time comparedto a single software-based design.
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