marziehlenjani / axbenchwithmicrokernels Goto Github PK

**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 (1) **bash cleanlib.sh**, and (2) **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 protected]*

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

    Amir Yazdanbakhsh ( http://www.cc.gatech.edu/~ayazdanb/ )
    Bradley Thwaites (http://users.ece.gatech.edu/~bthwaites3/)