Memristors provide a tempting solution for weighted synapse connections in neuromorphic
computing due to their size and non-volatile nature. However, memristors are unreliable in …

The synapse is a key element of neuromorphic computing in terms of efficiency and
accuracy. In this paper, an optimized current-controlled memristive synapse circuit is …

Spike-timing-dependent plasticity (STDP) is a widely used technique for online learning that
updates synapse weights based on the timing of pre-and post-synaptic spikes. Metal-oxide …

To create hardware platforms that are compact, power-efficient, and suitable for online
learning, we develop a spike-driven synaptic plasticity (SDSP) circuit for a Hafnium-Oxide …

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

The synapse is a key element circuit in any memristor-based neuromorphic computing
system. A memristor is a two-terminal analog memory device. Memristive synapses suffer …

The synapses are fundamental components of spiking neural networks (SNNs). Although
metal-oxide memristors, including Hafnium-Oxide (Hf0 2), offer potential in synaptic circuits …

Neuro-inspired computing systems provide an opportunity to mitigate the bottleneck of the
Von Neumann architecture. A memristor-based reconfigurable neuromorphic array (RNA) …

Reservoir computer (RC) is an emerging computing framework to optimize the training cost.
RC is a suitable solution for low-power devices such as edge devices. In addition, RC layer …

In this paper, we introduce a novel device architecture that merges memristive devices with
light-sensing surfaces, for energy-efficient motion recognition at the edge. Our light-sensing …