William Severa
ABSTRACT
Evolutionary algorithms have been shown to be an effective method for training (or configuring) spiking neural networks. There are, however, challenges to developing accessible, scalable, and portable solutions. We present an extension to the Fugu framework that wraps the NEAT framework, bringing evolutionary algorithms to Fugu. This approach provides a flexible and customizable platform for optimizing network architectures, independent of fitness functions and input data structures. We leverage Fugu's computational graph approach to evaluate all members of a population in parallel. Additionally, as Fugu is platform-agnostic, this population can be evaluated in simulation or on neuromorphic hardware. We demonstrate our extension using several classification and agent-based tasks. One task illustrates how Fugu integration allows for spiking pre-processing to lower the search space dimensionality. We also provide some benchmark results using the Intel Loihi platform.
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Index Terms
- Neuromorphic Population Evaluation using the Fugu Framework
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