Nonvolatile Memories in Spiking Neural Network Architectures: Current and Emerging Trends
M. Lakshmi Varshika, Federico Corradi, Anup Das
Abstract
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.
Topics & Concepts
Neuromorphic engineeringComputer scienceSpiking neural networkSpike (software development)Computer architectureAdaptabilityEfficient energy useArtificial neural networkNon-volatile memoryDistributed computingArtificial intelligenceEngineeringElectrical engineeringComputer hardwareSoftware engineeringEcologyBiologyAdvanced Memory and Neural ComputingFerroelectric and Negative Capacitance DevicesNeural Networks and Reservoir Computing