Nonvolatile Memories in Spiking Neural Network Architectures: Current and Emerging Trends

A sustainable computing scenario demands more energy-efficient processors. Neuromorphic systems mimic biological functions by employing spiking neural networks for achieving brain-like efficiency, speed, adaptability, and intelligence. Current trends in neuromorphic technologies address the challenges of investigating novel materials, systems, and architectures for enabling high-integration and extreme low-power brain-inspired computing. This review collects the most recent trends in exploiting the physical properties of nonvolatile memory technologies for implementing efficient in-memory and in-device computing with spike-based neuromorphic architectures.

Automated summary

Sustainable computing requires energy-efficient processors, and neuromorphic systems mimic biological functions for brain-like efficiency, speed, adaptability, and intelligence. Current neuromorphic technology trends focus on novel materials to enable high-integration and extreme low-power brain-inspired computing. Nonvolatile memory technologies are being used for efficient in-memory and in-device computing with spike-based neuromorphic architectures.