Introduction to Neuromorphic Computing¶

Alternative forms of computing have gained relevance in the last decade as traditional CPU and GPU architectures reach fabrication and physical limits, and the power consumption of compute clusters sharply increased with the rise of ML/AI algorithms. New computing paradigms such as quantum and analog computing, as well as dedicated hardware architectures offer paths to sidestep the limitations of classical computing. Neuromorphic computing is another paradigm competing in this area: based on replicating the behavior of biological neurons, it combines the advantages of analog and digital computing, resulting in computations that are inherently low power and well suited for solving a large number of problems and implementing a wide variety of algorithms including optimization algorithms, AI/ML, and more. Working in neuromorphic computing requires new ways of thinking and of approaching algorithm design.

This module is intended as a light introduction to neuromorphic computing, its basic concepts, advantages and applicability domains. Following this theoretical introduction, we will look in depth at the basic leaky integrate and fire neuron model and implement our own version in Python. Then, we have a look at PyNN, a Python API for setting up and running spiking neuron network simulations, which also helps to introduce some concepts in neuromorphic computing. Finally, to put it all together, we implement our own neuromorphic code to solve the classic map coloring problem.

and finally a couple of hands-on practical simulations based on Python code.

Prerequisites

Basic Python knowledge
Knowledge of the basic behavior of biological neurons is of advantage
Knowledge of optimization methods, constraint problems, and graph theory also of advantage

Module content¶

Software Setup

Setting Up Programming Environment

Neuromorphic Computing

Reference

Learning outcomes¶

This material is intended for those interested in neuromorphic computing in particular, as well as alternative forms of computing such as analog and quantum computing.

By the end of this module, learners should be able to:

Explain the basic elements of neuromorphic computing
Enumerate and justify the advantages of neuromorphic computing as a computing paradigm, its possible applications, and some of the software and hardware options available for running neuromorphic calculations
Implement their own simple LIF neuron model
Understand the basics of the PyNN Python library for constructing spiking neuron networks
Solve simple constraint optimization problems with a neuromorphic algorithm, as exemplified by the map coloring problem

See also¶

Credit

Author: Martin Paleico (Gesellschaft für wissenschaftliche Datenverarbeitung mbH Göttingen)
License: CC BY-SA 4.0 and MIT (also known as Expat)
Contributing and error reporting: Please send an email to hpc-support@gwdg.de

License

CC BY-SA for media and pedagogical material

Canonical URL: https://creativecommons.org/licenses/by-sa/4.0/

See the legal code

You are free to

Share — copy and redistribute the material in any medium or format for any purpose, even commercially.
Adapt — remix, transform, and build upon the material for any purpose, even commercially.
The licensor cannot revoke these freedoms as long as you follow the license terms.

Under the following terms

Attribution — You must give appropriate credit , provide a link to the license, and indicate if changes were made . You may do so in any reasonable manner, but not in any way that suggests the licensor endorses you or your use.
ShareAlike — If you remix, transform, or build upon the material, you must distribute your contributions under the same license as the original.
No additional restrictions — You may not apply legal terms or technological measures that legally restrict others from doing anything the license permits.

Notices

You do not have to comply with the license for elements of the material in the public domain or where your use is permitted by an applicable exception or limitation .

No warranties are given. The license may not give you all of the permissions necessary for your intended use. For example, other rights such as publicity, privacy, or moral rights may limit how you use the material.

This deed highlights only some of the key features and terms of the actual license. It is not a license and has no legal value. You should carefully review all of the terms and conditions of the actual license before using the licensed material.

MIT for source code and code snippets

MIT License

Permission is hereby granted, free of charge, to any person obtaining a copy of this software and associated documentation files (the “Software”), to deal in the Software without restriction, including without limitation the rights to use, copy, modify, merge, publish, distribute, sublicense, and/or sell copies of the Software, and to permit persons to whom the Software is furnished to do so, subject to the following conditions:

The above copyright notice and this permission notice shall be included in all copies or substantial portions of the Software.

THE SOFTWARE IS PROVIDED “AS IS”, WITHOUT WARRANTY OF ANY KIND, EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE.

Note

To module authors: For code you may use any OSI-approved license as mentioned in https://spdx.org/licenses/, such as Apache License 2.0, GNU GPLv3, MIT. Please make sure to update the deed above and LICENSE.code file accordingly.