\Sigma}{\Delta}$ Low Pas RNN

ΣΔ low-pass recurrent neural networks (RNNs) represent a specialized RNN architecture designed for efficient and accurate mapping onto neuromorphic hardware. Current research focuses on improving the precision and energy efficiency of these networks, often employing adaptive spiking neuron models and ΣΔ modulation for signal encoding. This approach is particularly relevant for resource-constrained applications requiring real-time inference, such as edge computing and embedded systems, where power efficiency is paramount. The development of robust and accurate ΣΔ lpRNNs promises significant advancements in low-power AI hardware and its deployment in various real-world scenarios.