/gosmann-frontiers2017

Automatic Optimization of the Computation Graph in the Nengo Neural Network Simulator

Primary LanguageJupyter Notebook

Automatic Optimization of the Computation Graph in the Nengo Neural Network Simulator

One critical factor limiting the size of neural cognitive models is the time required to simulate such models. To reduce simulation time, specialized hardware is often used. However, such hardware can be costly, not readily available, or require specialized software implementations that are difficult to maintain. Here, we present an algorithm that optimizes the computational graph of the Nengo neural network simulator, allowing simulations to run more quickly on commodity hardware. This is achieved by merging identical operations into single operations and restructuring the accessed data in larger blocks of sequential memory. In this way, a time speed-up of up to 6.8 is obtained. While this does not beat the specialized OpenCL implementation of Nengo, this optimization is available on any platform that can run Python. In contrast, the OpenCL implementation supports fewer platforms and can be difficult to install.

Requirements

To run the source code Python is required. Benchmarks were run with Python 3.4.2, while data analysis and plotting code was run with Python 3.6.1. The code might run as well with Python 2.7, but this was not tested.

The complete list of dependencies to run all parts is given in requirements.txt with the exact versions used.

Installation

It is best to use a newly created virtualenv for the installation.

  1. git clone --recursive https://github.com/ctn-archive/gosmann-frontiers2017.git
  2. cd gosmann-frontiers2017
  3. pip install -r requirements.txt
  4. pip install .
  5. cd spaun2.0/_spaun/arms/three_link
  6. pip install .

Reproducing results

Benchmarks

To run the benchmarks run psy-doit from the root folder of the project. This will take quite some time (up to a few days). The data will be stored in the files

  • psy-work/memory/result.npz,
  • psy-work/memory_spaun/result.npz,
  • psy-work/time_cconv/result.npz,
  • psy-work/time_nback/result.npz,
  • psy-work/time_spaun/result.npz.

Precomputed time_*/result.npz data files can be found in the data folder. The memory data files are not included due to their larger size. To just generate the memory data files you can run psy-doit memory memory_spaun.

Operator reduction

The reduction in operators for a model can be printed with python scripts/log_reduction.py <model> where model can be one of circ_conv, lorenz, nback, or spaun. This script supports additional arguments like --neuron-type. A list of all arguments can be printed with python scripts/log_reduction.py -h.

The data folder contains text files with the output for different models with different neurons types.

Total number of neurons

The total number of neurons in a model can be printed with python scripts/n_neurons <model>.

Figures and analysis

The analysis and plotting code is contained in Jupyter notebooks and can be opened with:

  1. cd notebooks
  2. jupyter notebook

The notebooks require the corresponding data files in the data folder. The time data is contained in this repository, but the memory data needs to be generated first and copied to that directory.