Designing Energy-Efficient Convolutional Neural Networks using Energy-Aware Pruning

by   Tien-Ju Yang, et al.

Deep convolutional neural networks (CNNs) are indispensable to state-of-the-art computer vision algorithms. However, they are still rarely deployed on battery-powered mobile devices, such as smartphones and wearable gadgets, where vision algorithms can enable many revolutionary real-world applications. The key limiting factor is the high energy consumption of CNN processing due to its high computational complexity. While there are many previous efforts that try to reduce the CNN model size or amount of computation, we find that they do not necessarily result in lower energy consumption, and therefore do not serve as a good metric for energy cost estimation. To close the gap between CNN design and energy consumption optimization, we propose an energy-aware pruning algorithm for CNNs that directly uses energy consumption estimation of a CNN to guide the pruning process. The energy estimation methodology uses parameters extrapolated from actual hardware measurements that target realistic battery-powered system setups. The proposed layer-by-layer pruning algorithm also prunes more aggressively than previously proposed pruning methods by minimizing the error in output feature maps instead of filter weights. For each layer, the weights are first pruned and then locally fine-tuned with a closed-form least-square solution to quickly restore the accuracy. After all layers are pruned, the entire network is further globally fine-tuned using back-propagation. With the proposed pruning method, the energy consumption of AlexNet and GoogLeNet are reduced by 3.7x and 1.6x, respectively, with less than 1 pruning the AlexNet with a reduced number of target classes can greatly decrease the number of weights but the energy reduction is limited. Energy modeling tool and energy-aware pruned models available at


page 1

page 2

page 3

page 4


Leveraging Structured Pruning of Convolutional Neural Networks

Structured pruning is a popular method to reduce the cost of convolution...

Architecture-aware Network Pruning for Vision Quality Applications

Convolutional neural network (CNN) delivers impressive achievements in c...

Designing Adaptive Neural Networks for Energy-Constrained Image Classification

As convolutional neural networks (CNNs) enable state-of-the-art computer...

Deep k-Means: Re-Training and Parameter Sharing with Harder Cluster Assignments for Compressing Deep Convolutions

The current trend of pushing CNNs deeper with convolutions has created a...

Energy Efficient Hardware Acceleration of Neural Networks with Power-of-Two Quantisation

Deep neural networks virtually dominate the domain of most modern vision...

Energy Consumption Analysis of pruned Semantic Segmentation Networks on an Embedded GPU

Deep neural networks are the state of the art in many computer vision ta...

Partition Pruning: Parallelization-Aware Pruning for Deep Neural Networks

Parameters of recent neural networks require a huge amount of memory. Th...

Please sign up or login with your details

Forgot password? Click here to reset