In an increasingly data-rich world the need for developing computing systems that cannot
only process, but ideally also interpret big data is becoming continuously more pressing …

Memristor-based neuromorphic computing could overcome the limitations of traditional von
Neumann computing architectures—in which data are shuffled between separate memory …

Lifelong on-device learning is a key challenge for machine intelligence, and this requires
learning from few, often single, samples. Memory-augmented neural networks have been …

Neuromorphic computers comprised of artificial neurons and synapses could provide a
more efficient approach to implementing neural network algorithms than traditional …

Artificial neural networks are notoriously power-and time-consuming when implemented on
conventional von Neumann computing systems. Consequently, recent years have seen an …

Memristive nanodevices can feature a compact multilevel nonvolatile memory function, but
are prone to device variability. We propose a novel neural network-based computing …

Memristors have emerged as a promising candidate for critical applications such as non-
volatile memory as well as non-Von Neumann computing architectures based on …

The recent decline in energy, size and complexity scaling of traditional von Neumann
architecture has resurrected considerable interest in brain-inspired computing. Artificial …

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

As a key building block of biological cortex, neurons are powerful information processing
units and can achieve highly complex nonlinear computations even in individual cells …