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pro vyhledávání: '"Hong, Deokki"'
Autor:
Hong, Deokki, Choi, Kanghyun, Lee, Hye Yoon, Yu, Joonsang, Park, Noseong, Kim, Youngsok, Lee, Jinho
Co-exploration of an optimal neural architecture and its hardware accelerator is an approach of rising interest which addresses the computational cost problem, especially in low-profile systems. The large co-exploration space is often handled by adop
Externí odkaz:
http://arxiv.org/abs/2301.09312
Autor:
Hong, Deokki
This work is for designing one-stage lightweight detectors which perform well in terms of mAP and latency. With baseline models each of which targets on GPU and CPU respectively, various operations are applied instead of the main operations in backbo
Externí odkaz:
http://arxiv.org/abs/2210.17151
Autor:
Choi, Kanghyun, Lee, Hye Yoon, Hong, Deokki, Yu, Joonsang, Park, Noseong, Kim, Youngsok, Lee, Jinho
Model quantization is considered as a promising method to greatly reduce the resource requirements of deep neural networks. To deal with the performance drop induced by quantization errors, a popular method is to use training data to fine-tune quanti
Externí odkaz:
http://arxiv.org/abs/2203.17008
Model quantization is known as a promising method to compress deep neural networks, especially for inferences on lightweight mobile or edge devices. However, model quantization usually requires access to the original training data to maintain the acc
Externí odkaz:
http://arxiv.org/abs/2111.02625
To cope with the ever-increasing computational demand of the DNN execution, recent neural architecture search (NAS) algorithms consider hardware cost metrics into account, such as GPU latency. To further pursue a fast, efficient execution, DNN-specia
Externí odkaz:
http://arxiv.org/abs/2009.06237