This Deep Neural Network Energy Estimation Tool is used for evaluating and designing energy-efficient deep neural networks that are critical for embedded deep learning processing. Energy estimation was used in the development of the energy-aware pruning method (Yang et al., CVPR 2017), which reduced the energy consumption of AlexNet and GoogLeNet by 3.7x and 1.6x, respectively, with less than 1% top-5 accuracy loss. This website provides a simplified version of the energy estimation tool for shorter runtime (around 10 seconds).
To support the variety of toolboxes, this tool takes a single network configuration file. The network configuration file is a txt file, where each line denotes the configuration of a CONV/FC layer. The format of each line is:
Therefore, there will be 25 entries separated by commas in each line.
After creating your text file, follow these steps to upload your text file and run the estimation model:
The estimation model takes approximately 10 seconds to run, please be patient and only click the run button once. Upon successful completion a download button will appear at the top of the page which you can click to download a zip file with the results to your computer, see below for more information on the output. Error messages will be displayed in red text below the buttons if something goes wrong.
The tool generates:
Click the links below for examples of the network configuration file:
For the runtime consideration, this online tool is a simplified version of the tool used in the CVPR 2017 paper. Therefore, there will be a small difference between the estimated energy values.