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

The recent devastating pandemic has drastically reminded humanity of the importance of
constant scientific and technological progress. A strong interdisciplinary dialogue between …

Memristive devices have shown great promise to facilitate the acceleration and improve the
power efficiency of Deep Learning (DL) systems. Crossbar architectures constructed using …

Synaptic plasticity is envisioned to bring about learning and memory in the brain. Various
plasticity rules have been proposed, among which spike-timing-dependent plasticity (STDP) …

Memristive devices arranged in cross-bar architectures have shown great promise to facilitate
the acceleration and improve the power efficiency of Deep Learning (DL) systems for …

Deep Neural Networks (DNNs) have recently achieved remarkable performance in a myriad
of applications, ranging from image recognition to language processing. Training such …

Stochastic Computing (SC) is a computing paradigm that allows for the low-cost and low-power
computation of various arithmetic operations using stochastic bit streams and digital logic…

Deep Learning (DL) systems have demonstrated unparalleled performance in many challenging
engineering applications. As the complexity of these systems inevitably increase, they …

During the past two decades, epileptic seizure detection and prediction algorithms have
evolved rapidly. However, despite significant performance improvements, their hardware …

Neuromorphic hardware platforms have demonstrated significant promise in cognitive tasks
such as visual processing and classification. These platforms usually consist of several …