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**AxBench** is a benchmark suite combined with the necessary annotations and compilation workflow 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. *** === 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] *** === 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. *** === 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 aslo need to run **bash make_all.sh** inside the **fann.template** directory. 4) 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. *** === Parrot Annotations ===*** All the applications come with the necessary annotations for neural network transformation. We use **pragma** keyword to mark the region of the code which needs to be transformed to neural network representation. *** === 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 <application name>**. 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 <application name>** to build the application. 6) Run ** bash run.sh run <application name>** 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@example.com* *** === Maintained by === *** Amir Yazdanbakhsh ( http://www.cc.gatech.edu/~ayazdanb/ )