The volume, veracity, variability, and velocity of data produced from the ever increasing
network of sensors connected to Internet pose challenges for power management …

On metrics of density and power efficiency, neuromorphic technologies have the potential to
surpass mainstream computing technologies in tasks where real-time functionality …

Advanced computing systems have long been enablers for breakthroughs in artificial
intelligence (AI) and machine learning (ML) algorithms, either through sheer computational …

Recent studies have shown that synaptic unreliability is a robust and sufficient mechanism
for inducing the stochasticity observed in cortex. Here, we introduce Synaptic Sampling …

Recent theoretical studies have shown that probabilistic spiking can be interpreted as
learning and inference in cortical microcircuits. This interpretation creates new opportunities …

Diverse models have been proposed over the past years to explain the exhibiting behavior
of memristors, the fourth fundamental circuit element. The models varied in complexity …

Network of neurons in the brain apply—unlike processors in our current generation of
computer hardware—an event-based processing strategy, where short pulses (spikes) are …

Biological neural networks outperform current computer technology in terms of power
consumption and computing speed while performing associative tasks, such as pattern …

As information technology is moving toward the era of big data, the traditional Von-Neumann
architecture shows limitations in performance. The field of computing has already struggled …

Neuromorphic computing has become an attractive candidate for emerging computing
platforms. It requires an architectural perspective, meaning the topology or hyperparameters …