<ul><li>Spiking Neural Networks (SNNs) are highly energy-efficient during inference, making them suitable for deployment on neuromorphic hardware.</li><li>A benchmark study evaluates two quantization pipelines for fixed-point computations in SNNs optimized for the SpiNNaker2 chip.</li><li>The first approach employs post training quantization (PTQ) with percentile-based threshold scaling.</li><li>The second method uses quantization aware training (QAT) with adaptive threshold scaling, both achieving accurate 8-bit on-chip inference.</li></ul>

Efficient Deployment of Spiking Neural Networks on SpiNNaker2 for DVS Gesture Recognition Using Neuromorphic Intermediate Representation

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