Mapping high-performance RNNs to in-memory neuromorphic chips

Understand

The increasing need for compact and low-power computing solutions for machine learning applications has triggered significant interest in energy-efficient neuromorphic systems.

• However, most of these architectures rely on spiking neural networks, which typically perform poorly compared to their non-spiking counterparts in terms of accuracy.
• In this paper, we propose a new adaptive spiking neuron model that can be abstracted as a low-pass filter.
• This abstraction enables faster and better training of spiking networks using back-propagation, without simulating spikes.