PeleNet: A Reservoir Computing Framework for Loihi
This framework simplifies programming neuromorphic hardware for researchers and developers working with reservoir computing on Loihi, addressing a current challenge in the field.
This paper introduces PeleNet, a Python-based framework built on Intel's NxSDK, designed to simplify reservoir computing on Loihi neuromorphic hardware. It automates network distribution across cores and chips, abstracting away low-level hardware details for users.
High-level frameworks for spiking neural networks are a key factor for fast prototyping and efficient development of complex algorithms. Such frameworks have emerged in the last years for traditional computers, but programming neuromorphic hardware is still a challenge. Often low level programming with knowledge about the hardware of the neuromorphic chip is required. The PeleNet framework aims to simplify reservoir computing for the neuromorphic hardware Loihi. It is build on top of the NxSDK from Intel and is written in Python. The framework manages weight matrices, parameters and probes. In particular, it provides an automatic and efficient distribution of networks over several cores and chips. With this, the user is not confronted with technical details and can concentrate on experiments.