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 powerdevices. 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 and the inability to measure power and performance accurately and consistently across devices. We present a novel evaluation framework for measuring energy and performance for deep neural networks using ARMs Streamline Performance Analyser integrated with standard deep learning frameworks such as Caffe and CuDNNv5 . We apply the framework to study the execution behaviour of SqueezeNet on the Maxwell GPU of the NVidia Jetson TX1 , on an image classification task (also known as inference) and demonstrate the ability to measure energy of specific layers of the neural network .
There is a huge demand for on-device execution of deep learning algorithms on mobile and embedded platforms. These devices present constraints on the application due to limited resources and power. Hence, developing energy-efficient solutions to address this issue will require innovation in algorithmic design, software and hardware. Such innovation requires benchmarking and characterization of Deep Neural Networks based on performance and energy-consumption alongside accuracy. However, current benchmarks studies in existing deep learning frameworks (for example, Caffe, Tensorflow, Torch and others) are based on performance of these applications on high-end CPUs and GPUs. In this work, we introduce a benchmarking framework called "SyNERGY" to measure the energy and time of 11 representative Deep Convolutional Neural Networks on embedded platforms such as NVidia Jetson TX1. We integrate ARM's Streamline Performance Analyser with standard deep learning frameworks such as Caffe and CuDNNv5 , to study the execution behaviour of current deep learning models at a fine-grained level (or specific layers) on image processing tasks. In addition, we build an initial multi-variable linear regression model to predict energy consumption of unseen neural network models based on the number of SIMD instructions executed and main memory accesses of the CPU cores of the TX1 with an average relative test error rate of 8.04 +/- 5.96 \%. Surprisingly, we find that it is possible to refine the model to predict the number of SIMD instructions and main memory accesses solely from the application's Multiply-Accumulate (MAC) counts, hence, eliminating the need for actual measurements. Our predicted results demonstrate 7.08 +/- 6.0 \% average relative error over actual energy measurements of all 11 networks tested, except MobileNet. By including MobileNet the average relative test error increases to 17.33 +/- 12.2 \% .
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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
There is a huge demand for on-device execution of deep learning algorithms on mobile and embedded platforms. These devices present constraints on the application due to limited resources and power. Hence, developing energy-efficient solutions to address this issue will require innovation in algorithmic design, software and hardware. Such innovation requires benchmarking and characterization of Deep Neural Networks based on performance and energy-consumption alongside accuracy. However, current benchmarks studies in existing deep learning frameworks (for example, Caffe, Tensorflow, Torch and others) are based on performance of these applications on
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GPUs or servers and require additional processing steps to deploy them on low powerdevices. 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 and the inability to measure power and performance accurately and consistently across devices. We present a novel evaluation framework for measuring energy and performance for deep neural networks using ARMs
CPUs and GPUs. In this work, we introduce a benchmarking framework called "SyNERGY" to measure the energy and time of 11 representative Deep Convolutional Neural Networks on embedded platforms such as NVidia Jetson TX1. We integrate ARM's
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integrated
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. We apply the framework
,
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SqueezeNet on the Maxwell GPU of the NVidia Jetson
current deep learning models at a fine-grained level (or specific layers) on image processing tasks. In addition, we build an initial multi-variable linear regression model to predict energy consumption of unseen neural network models based on the number of SIMD instructions executed and main memory accesses of the CPU cores of the
R
, on an image classification task (also known as inference) and demonstrate the ability to measure energy of specific layers of the neural network
with an average relative test error rate of 8.04 +/- 5.96 \%. Surprisingly, we find that it is possible to refine the model to predict the number of SIMD instructions and main memory accesses solely from the application's Multiply-Accumulate (MAC) counts, hence, eliminating the need for actual measurements. Our predicted results demonstrate 7.08 +/- 6.0 \% average relative error over actual energy measurements of all 11 networks tested, except MobileNet. By including MobileNet the average relative test error increases to 17.33 +/- 12.2 \%