tabla

Welcome to TABLA!

Tabla is an innovative framework that accelerates a class of statistical machine learning algorithms. It consists of the accelerator template, domain-specific language, and model compiler.

This document will help you get up and running.

Step 0: Check prerequisites

The following dependencies must be met by your system:

• python >= 3.7 (For PEP 560 support)

Step 1: Clone the VeriGOOD-ML source code

$ git clone --recurse-submodules https://github.com/VeriGOOD-ML/public
$ cd public/tabla

Step 2: Create a Python virtualenv

Note: You may choose to skip this step if you are doing a system-wide install for multiple users. Please DO NOT skip this step if you are installing for personal use and/or you are a developer.

$ python -m venv general
$ source general/bin/activate
$ python -m pip install pip --upgrade

Step 3: Install TABLA

If you already have a working installation of Python 3.7 or Python 3.8, the easiest way to install TABLA is:

$ pip install -e .

Step 4: Compile a benchmark using TABLA

You can compile a TABLA benchmark by running the following commands, where <benchmark_name> is one of backprop, linear, logistic, reco, svm, svm_wifi and <feature_size> corresponds to the feature size of the target benchmark:

$ python benchmarks/run_benchmark.py --benchmark <benchmark_name> --feature_size <feature_size>

Compiled output will be stored in the tabla/benchmarks/compilation_output/ directory.

Step 5: Simulate a benchmark using TABLA

After compiling the benchmark, you can run a software simulation of the benchmark by running the following command:

$ python benchmarks/simulate_benchmark.py --benchmark <benchmark_name> --feature_size <feature_size>