This article reviews the resistive random-access memory (ReRAM) technology initialization
back in the 1960s and its heavily focused research and development from the early 2000s …

Recently, both industry and academia have proposed several different neuromorphic
systems to execute machine learning applications that are designed using Spiking Neural …

Neuromorphic hardware implements biological neurons and synapses to execute a spiking
neural network (SNN)-based machine learning. We present SpiNeMap, a design …

Neuromorphic computing systems are embracing memristors to implement high density and
low power synaptic storage as crossbar arrays in hardware. These systems are energy …

Limited endurance of resistive RAM (RRAM) is a major challenge for future computing
systems. Using thorough endurance tests that incorporate fine-grained read operations at …

Machine learning applications that are implemented with spike-based computation model,
eg, Spiking Neural Network (SNN), have a great potential to lower the energy consumption …

Spiking Neural Networks (SNNs) are an emerging computation model that uses event-
driven activation and bio-inspired learning algorithms. SNN-based machine learning …

The design of many-core neuromorphic hardware is becoming increasingly complex as
these systems are now expected to execute large machine-learning models. A predictable …

Phase change memory (PCM) is a scalable non-volatile memory technology that has low
access latency (like DRAM) and high capacity (like Flash). Writing to PCM incurs …

Artificial intelligence (AI) and Machine Learning (ML) are becoming pervasive in today's
applications, such as autonomous vehicles, healthcare, aerospace, cybersecurity, and many …