Deep learning has become ubiquitous, touching daily lives across the globe. Today,
traditional computer architectures are stressed to their limits in efficiently executing the …

The global demand for data storage and processing is increasing exponentially. To deal
with this challenge, massive efforts have been devoted to the development of advanced …

Modern computation based on von Neumann architecture is now a mature cutting-edge
science. In the von Neumann architecture, processing and memory units are implemented …

Analog in-memory computing—a promising approach for energy-efficient acceleration of
deep learning workloads—computes matrix-vector multiplications but only approximately …

Artificial intelligence and other data-intensive applications have escalated the demand for
data storage and processing. New computing devices, such as phase-change random …

Phase change memory has been developed into a mature technology capable of storing
information in a fast and non-volatile way,–, with potential for neuromorphic computing …

High‐performance artificial synaptic devices are indispensable for develo** neuromorphic
computing systems with high energy efficiency. However, the reliability and variability issues …

Phase‐change material (PCM) devices are one of the most mature nonvolatile memories.
However, their high power consumption remains a bottleneck problem limiting the data …

Data-centric applications are pushing the limits of energy-efficiency in today's computing
systems, including those based on phase-change memory (PCM). This technology must …

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