AxBench is a benchmark suite with the necessary annotations for approximate computing. We develop AxBench in C++, aiming to provide a set of representative applications from various domains to explore different aspect of the approximate computing. AxBench is developed in Alternative Computing Technologies (ACT) Laboratory, Georgia Institute of Technology.

*** === Papers === ***

We actively work on AxBench to add more applications from different domains (e.g. Computer Vision, Data Analytics, Multimedia, Web Search, Finance, etc.). We will also be working on adding different features to this benchmark suite in order to enable researchers to study different aspects of approximate computing. As a courtesy to the developers, we ask that you please cite our papers from MICRO'12 describing the suite:

      1. H. Esmaeilzadeh, A. Sampson, L. Ceze, D. Burger, 
          "Neural acceleration for general-purpose approximate programs", MICRO 2012.

*** === Applications === ***

1. Black-Scholes [Financial Analysis]: This application calculates the price of European options. We adapted this benchmark from Parsec benchmark suite to use for approximate computing purpose.
2. FFT [Signal Processing]: This application calculates the radix-2 Cooley-Turkey Fast Fourier for a set of random floating point numbers.
3. Inversek2j [Robotics]: This applications find the coordinate of a 2-joint arm given their angle.
4. Jmeint [3D Gaming]: This application detects the intersection of two 3D triangles.
5. JPEG encoder [Compression]: This application compress an image.
6. K-means [Machine Learning]: This application performs K-means clustering on a set of random (r, g, b) values.
7. Sobel [Image Processing]: Sobel filter detects the edges of a color image.

*** === Dependencies === ***

AxBench was developed on Ubuntu Linux (this release was tested with Ubuntu version 12.04). In principal, AxBench should work with any Linux distribution as long as the following software dependencies are satisfied.

1. Boost Libraries [version 1.49 or higher]
2. Python [version 2.7 or higher]
3. G++ [4.6 or higher]
4. Fast Artificial Neural Network Library (FANN) [version 2.2.0 or higher]

*** === Build and Run AxBench ===***

1. After downloading the AxBench, please go to the parrot.c/src directory and run bash buildlib.sh. It will create a static library which will be later used to execute the Parrot transformation on the applications.

2. Then, modify config.mk in the applications folder with the location of the Parrot and FANN library.

3. You are set to use AxBench. You can simply execute the run.sh script to make or run each of the applications.

*** === Compilation Parameters ===***

There are some parameters that need to be specified by the user during the compilation. Here you can see a brief explanation about each of these parameters.

1. ** Learning rate: ** Rate of learning for RPROP algorithm.

2. ** Epoch number: ** Number of epochs for training.

3. ** Sampling Rate: ** The percentage of data which is used for training and testing.

4. ** Test data fraction: ** The percentage of sampled data which is used for testing the obtained neural network.

5. ** Maximum number of layers: ** The maximum number of layers in the neural network.

6. ** Maximum number of neurons per layer: ** The maximum number of neurons per each hidden layer.

*** === Adding new benchmarks ===***

You can easily add new benchmarks to AxBench. These are the necessary steps that need to be followed.

1. Run ** bash run.sh setup **.

2. Put the source files into the src directory and annotate the region of interest with the Parrot semantics.

3. Put the train and test datasets into their corresponding folders (train.data and test.data).

4. Create ** Makefile **, Makefile_nn, run_observation.sh, and run_NN.sh. You may get help on how to create these files from other application directories.

5. Run ** bash run.sh make ** to build the application.

6. Run ** bash run.sh run ** to apply Parrot transformation and replace the region of interest with a neural network.

*** === Software License === ***

The license is a free non-exclusive, non-transferable license to reproduce, use, modify and display the source code version of the Software, with or without modifications solely for non-commercial research, educational or evaluation purposes. The license does not entitle Licensee to technical support, telephone assistance, enhancements or updates to the Software. All rights, title to and ownership interest in Software, including all intellectual property rights therein shall remain in Georgia Institute of Technology.

*** === Questions === ***

Please forward your questions to: [email protected]

*** === Maintained by === ***

Amir Yazdanbakhsh ( http://www.cc.gatech.edu/~ayazdanb/ )