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# README #
**AxBench** is a benchmark suite 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.
This README would normally document whatever steps are necessary to get your application up and running.
*** === Papers === ***
### What is this repository for? ###
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 and ISCA'14 describing the suite:
* Quick summary
* Version
* [Learn Markdown](https://bitbucket.org/tutorials/markdowndemo)
1. H. Esmaeilzadeh, A. Sampson, L. Ceze, D. Burger,
"Neural acceleration for general-purpose approximate programs", MICRO 2012.
2. R. Amant, A. Yazdanbakhsh, J. Park, B. Thwaites, H. Esmaeilzadeh, A. Hassibi, L. Ceze, D. Burger,
"General-Purpose Code Acceleration with Limited-Precision Analog Computation", ISCA 2014.
### How do I get set up? ###
*** === Applications === ***
* Summary of set up
* Configuration
* Dependencies
* Database configuration
* How to run tests
* Deployment instructions
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.
### Contribution guidelines ###
*** === Dependencies === ***
* Writing tests
* Code review
* Other guidelines
**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.
### Who do I talk to? ###
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]
* Repo owner or admin
* Other community or team contact
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*** === Build and Run AxBench ===***
1) After downloading the **AxBench**, please go to the *anpu.compiler* directory and run the Makefile. It will create a static library which will be later used to execute the applications on analog neural network model.
2) Then, in the root directory specify the **full path** location of the *anpu.compiler* and the location of the FANN library in *config.mk*.
3) You are set to use **AxBench**. You can simply execute the *run.sh* script to make or run each of the applications.
**AxBench** can be run in precise or various approximate modes. Currently, we support two modes, namely *NPU_OBSERVATION* and *NPU_FANN*. The observation mode simply runs the applications in the precise mode and generates the precise outputs in the data directory. The FANN mode runs the applications on a NN and generates the approximate output and reports the output error. The NN configuration for each application are placed in the corresponding application directory inside the *cfg* directory.
*** === 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: *a.yazdanbakhsh@gatech.edu*
*** === Maintained by === ***
Amir Yazdanbakhsh ( http://www.cc.gatech.edu/~ayazdanb/ )
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