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A python library for computational neural graphs.
Fugu is a high-level framework specifically designed for developing spiking circuits in terms of computation graphs. Accordingly, with a base leaky-integrate-and fire (LIF) neuron model at its core, neural circuits are built as bricks. These foundational computations are then combined and composed as scaffolds to construct larger computations. This allows us to describe spiking circuits in terms of neural features common to most NMC architectures rather than platform specific designs. In addition to architectural abstraction, the compositionality concept of Fugu not only facilitates a hierarchical approach to functionality development but also enables adding pre and post processing operations to overarching neural circuits. Such properties position Fugu to help explore under what parameterization or scale a neural approach may offer an advantage. For example, prior work has analyzed neural algorithms for computational kernels like sorting, optimization, and graph analytics identifying different regions in which a neural advantage exists accounting for neural circuit setup, timing, or other factors.
A full list of dependencies is listed in requirements.txt. The high level dependencies are:
- Numpy
- NetworkX
- Pandas
Some of the examples additionally require Jupyter and matplotlib.
git clone http://github.com/SNL-NERL/Fugu.git
cd Fugu
pip install -r requirements.txt
pip install --upgrade .
Documentation is currently spread across several files. We are working on including docstrings on all the classes and methods.
For now, you can check:
- This README.md
- The examples folder
- API Documentation
The Scaffold object is a graph that contains bricks at each node. In reality, the Scaffold is only responsible for the organization of bricks. All functionality is held in the bricks themselves.
Each Brick represents one computational function. Bricks are attached to a Scaffold. Bricks have certain key properties:
- metadata: A dictionary containing information such as the input and output sizes, circuit depth (if defined), and the types of codings.
- upported_codings: A list of supported codings for this brick. A complete list of codings is avialable at input_coding_types .
- is_built: A simple boolean saying whether or not the brick as been built
- name: A string representing the brick
This function forms the section of the graph corresponding to the brick.
Input parameters:
- graph: graph that is being built
- metadata: dictionary containing relevant dimensionality information
- control_nodes: A list of dictionaries of neurons that transmit control signals. A 'done' signal (Generally one from each input) is included in
control_nodes[i]['complete']. - input_lists: A list of lists of input neurons. Each neuron is marked with a local index used for encodings.
- input_codings: A list of types of input codings. See input_coding_types
Output: A tuple (graph, metadata, complete_node, output_lists, output_codings)
- graph: Graph that is being built
- metadata: Dictionary containing relevant metadata information
- control_nodes: A dictionary of lists of neurons that transmists control information (see below)
- output_lists: A list of lists of output neurons. Each neuron is marked with a local index used for encodings.
- output_codings: A list of types of codings. See input_coding_types
We've seen through experience that it can be extremely helpful to have neurons relay control
information between bricks. These neurons should be included in the control_nodes. Regular
rules apply to these neurons (e.g. naming must be globally unique and indices are locally unique).
control_nodes is a list of dictionaries. Each entry in the list corresponds with
an input (if calling Brick.build) or an output (if returning from Brick.build).
| Key | Required | Description |
|---|---|---|
| 'complete' | All Inputs/Outputs | A neuron that fires when a brick is done processing. |
| 'begin' | Temporally-coded Inputs/Outputs | A neuron that fires when a brick begins providing output. |
We hope that as Fugu develops it will become a useful tool for the spiking neuromorphic field.
While the Fugu team is small, we are always interested in collaborations where possible. If you have questions or are interested in starting a collaboration, please e-mail us at wg-fugu@sandia.gov.
If you are not a collaborator but would like to contribute, you are welcome to create an github issue or e-mail to discuss. In many cases, extensions to Fugu should not be incoporated into the core of Fugu, see http://sandialabs.github.io/Fugu/build/html/contributing.html.
If you use Fugu for your research, please cite the following paper:
Aimone, James B., William Severa, and Craig M. Vineyard. “Composing neural algorithms with Fugu.” Proceedings of the International Conference on Neuromorphic Systems. 2019.