HfO 2-based resistive switching memory (RRAM) combines several outstanding properties,
such as high scalability, fast switching speed, low power, compatibility with complementary …

Many in-memory computing frameworks demand electronic devices with specific switching
characteristics to achieve the desired level of computational complexity. Existing memristive …

Modern computation based on von Neumann architecture is now a mature cutting-edge
science. In the von Neumann architecture, processing and memory units are implemented …

The advent of dedicated Deep Learning (DL) accelerators and neuromorphic processors
has brought on new opportunities for applying both Deep and Spiking Neural Network …

Learning is a fundamental component of creating intelligent machines. Biological
intelligence orchestrates synaptic and neuronal learning at multiple time scales to self …

Real-world sensory-processing applications require compact, low-latency, and low-power
computing systems. Enabled by their in-memory event-driven computing abilities, hybrid …

With the arrival of the Internet of Things (IoT) and the challenges arising from Big Data,
neuromorphic chip concepts are seen as key solutions for co** with the massive amount …

Dedicated hardware implementations of spiking neural networks that combine the
advantages of mixed-signal neuromorphic circuits with those of emerging memory …

In neuromorphic computing, different learning mechanisms are being widely adopted to
improve the performance of a specific application. Among these techniques, spike-timing …

Spike-timing-dependent plasticity (STDP) is a popular approach for online learning that
determines synaptic weight updates based on the relative timing of temporal events of pre …