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Structural plasticity on an accelerated analog neuromorphic hardware system.

Sebastian Billaudelle¹, Benjamin Cramer¹, Mihai A Petrovici²

¹Kirchhoff-Institute for Physics, Heidelberg University, Germany.

Neural Networks : the Official Journal of the International Neural Network Society

|October 22, 2020

Summary

This summary is machine-generated.

Neuromorphic devices offer faster neural network simulations. This study introduces structural plasticity to optimize resource allocation on the BrainScaleS-2 system, improving computational efficiency and network topology for supervised learning.

Keywords:

BrainScaleS Neural networks Receptive fields Spiking Structural plasticity

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Area of Science:

Computational neuroscience
Machine learning
Neuromorphic engineering

Background:

Neuromorphic devices offer accelerated and scalable alternatives to traditional neural network simulations.
Intrinsic limitations in neural connectivity and synaptic capacity exist in neuromorphic hardware.
Optimizing resource allocation under these constraints is crucial for efficient operation.

Purpose of the Study:

To present a strategy for achieving structural plasticity in neuromorphic systems.
To optimize resource allocation by rewiring synaptic connections while maintaining constant neuronal fan-in and sparsity.
To implement and evaluate this strategy on the BrainScaleS-2 analog neuromorphic system.

Main Methods:

Implemented a structural plasticity algorithm on an embedded digital processor within the BrainScaleS-2 system.
Utilized a mixed-signal substrate of spiking neurons and synapse circuits.
Evaluated the implementation in a supervised learning scenario.

Main Results:

Demonstrated the ability to optimize network topology based on training data characteristics.
Showcased improved overall computational efficiency of the neuromorphic system.
Successfully managed resource allocation under hardware constraints through dynamic rewiring.

Conclusions:

Structural plasticity is an effective strategy for optimizing neuromorphic hardware.
The implemented algorithm enhances adaptability and efficiency in spiking neural networks.
This approach addresses the inherent limitations of neuromorphic devices for scalable AI.