최근 임베디드 환경에서 딥 러닝을 적용하고자 하는 요구가 증가하고 있다. 임베디드와 같은 제한적인 환경에서 딥 러닝 연산을 효율적으로 수행하기 위해서 Arm의 big.LITTLE과 같은 이기종 멀티코어 CPU 아키텍처가 널리 활용되고 있다. Arm은 딥 러닝 연산을 최적으로 수행하기 위해 Arm Compute Library(ACL)를 제공하고 있지만, big.LITTLE 구조를 가진 하드웨어의 잠재력을 충분히 활용하지는 못하고 있다. 본 논문은 각 하드웨어에 최적인 실행 커널과 스케줄을 자동으로 결정하기 위한 프로파일 기반 탐색 방법을 제안한다. 실험은 Tinker Edge R, Odroid N+, Snapdragon 865 HDK 보드에서 AlexNet, VGG16, MobileNetV2, GoogleNet 모델을 대상으로 진행하였으며, 모든 경우에서 제안된 방법이 기존의 방법보다 최대 266% 성능 향상을 보임을 확인하였다. 본 연구의 결과를 통해 임베디드 기기에서 저비용, 저전력, 고성능의 딥 러닝 수행이 가능할 것으로 기대한다.
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