The demand for data processing in high-performance computing is growing rapidly.
Extrapolating these trends to the long term suggests that a switch, which is more energy …

Safety-critical sensory applications, like medical diagnosis, demand accurate decisions from
limited, noisy data. Bayesian neural networks excel at such tasks, offering predictive …

The spatiotemporal nature of neuronal behavior in spiking neural networks (SNNs) makes
SNNs promising for edge applications that require high energy efficiency. To realize SNNs …

The growing adoption of data-driven applications, such as artificial intelligence (AI), is
transforming the way we interact with technology. Currently, the deployment of AI and …

The rich dynamics of magnetic materials makes them promising candidates for neural
networks that, like the brain, take advantage of dynamical behaviors to efficiently compute …

Analog neuromorphic computing systems emulate the parallelism and connectivity of the
human brain, promising greater expressivity and energy efficiency compared to those of …

Recent development in neural networks (NNs) has led to their widespread use in critical and
automated decision-making systems, where uncertainty estimation is essential for …

Biological neural networks effortlessly tackle complex computational problems and excel at
predicting outcomes from noisy, incomplete data. Artificial neural networks (ANNs), inspired …

Creating stochastic neural networks with the help of probabilistic bits | Nature Electronics
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Device and algorithm co-design aims to develop energy-efficient hardware that directly
implements complex algorithms and optimizes algorithms to match the hardware's …