FrostRune: An Asymmetric Translational Framework for Spiking Neural Networks from High-Level Models to FPGA Deployment
Year: 2025
Type: conference-paper
Abstract: Spiking Neural Networks (SNNs) provide energy-efficient neuromorphic computing, but their deployment on FPGAs requires specialised hardware knowledge. We introduce FrostRune, a framework for automatically converting Python-based SNN descriptions into FPGA-ready VHDL code using the YAML format. Our experiments on the MNIST-DVS dataset demonstrate that FrostRune achieves high accuracy and behavioural equivalence, while significantly reducing latency, power consumption, and resource usage compared to traditional HDL coding approaches. This work lowers the barrier to FPGA-based neuromorphic computing and enables efficient SNN deployment.
Authors: Harrigan, Shane, Coleman, Sonya, Kerr, Dermot, Quinn, JP, Madden, Kyle, Liu, Shuo
Language: English
Subfield: Electrical and Electronic Engineering
Field: Engineering
Domain: Physical Sciences
Open Access status: green
OpenAlex ID: W7047391490
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