Artificial Intelligence (AI) is currently experiencing a bloom driven by deep learning (DL)
techniques, which rely on networks of connected simple computing units operating in …

Machine learning, particularly in the form of deep learning (DL), has driven most of the
recent fundamental developments in artificial intelligence (AI). DL is based on computational …

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 …

Reconfigurable devices offer the ability to program electronic circuits on demand. In this
work, we demonstrated on-demand creation of artificial neurons, synapses, and memory …

In-memory computing using resistive memory devices is a promising non-von Neumann
approach for making energy-efficient deep learning inference hardware. However, due to …

Analog hardware accelerators, which perform computation within a dense memory array,
have the potential to overcome the major bottlenecks faced by digital hardware for data …

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 constant drive to achieve higher performance in deep neural networks (DNNs) has led
to the proliferation of very large models. Model training, however, requires intensive …

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

This paper reports 2bits/cell ferroelectric FET (FeFET) devices with 500 ns write pulse of
maximum amplitude 4.5 V for inference-engine applications. FeFET devices were fabricated …