Fine-grained energy profiling for deep convolutional neural networks on the Jetson TX1
Energy-use is a key concern when migrating current deep learning applications onto low
power heterogeneous devices such as a mobile device. This is because deep neural
networks are typically designed and trained on high-end GPUs or servers and require
additional processing steps to deploy them on low power devices. Such steps include the
use of compression techniques to scale down the network size or the provision of efficient
device-specific software implementations. Migration is further aggravated by the lack of tools …
power heterogeneous devices such as a mobile device. This is because deep neural
networks are typically designed and trained on high-end GPUs or servers and require
additional processing steps to deploy them on low power devices. Such steps include the
use of compression techniques to scale down the network size or the provision of efficient
device-specific software implementations. Migration is further aggravated by the lack of tools …
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