In this article, we review the recent progress of ferroelectric field-effect transistors (FeFETs)
based on ferroelectric hafnium oxide (HfO 2), ten years after the first report on such a device …

Traditional computing systems based on the von Neumann architecture are fundamentally
bottlenecked by data transfers between processors and memory. The emergence of data …

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 …

Traditional neural networks require enormous amounts of data to build their complex
map**s during a slow training procedure that hinders their abilities for relearning and …

Nearest neighbor (NN) search computations are at the core of many applications such as
few-shot learning, classification, and hyperdimensional computing. As such, efficient …

The finding of the robust ferroelectricity in HfO 2-based thin films is fantastic from the view
point of both the fundamentals and the applications. In this review article, the current …

Homomorphic encryption (HE) allows direct computations on encrypted data. Despite
numerous research efforts, the practicality of HE schemes remains to be demonstrated. In …

Brain-inspired cognitive computing has so far followed two major approaches-one uses
multi-layered artificial neural networks (ANNs) to perform pattern-recognition-related tasks …

Machine learning and data analytics applications increasingly suffer from the high latency
and energy consumption of conventional von Neumann architectures. Recently, several in …

Transformer networks have outperformed recurrent and convolutional neural networks in
terms of accuracy in various sequential tasks. However, memory and compute bottlenecks …