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 …

In-memory computing (IMC) refers to non-von Neumann architectures where data are
processed in situ within the memory by taking advantage of physical laws. Among the …

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 …

Tree-based machine learning techniques, such as Decision Trees and Random Forests, are
top performers in several domains as they do well with limited training datasets and offer …

Memristors, and other emerging memory technologies, can be used to create energy-
efficient implementations of neural networks. However, for certain edge applications (in …

Specialized function gradient computing hardware could greatly improve the performance of
state-of-the-art optimization algorithms. Prior work on such hardware, performed in the …

Convolutional neural networks (CNNs) have been widely used in image recognition and
processing tasks. Memristor-based CNNs accumulate the advantages of emerging …

Memristor-based neural networks provide an exceptional energy-efficient platform for
artificial intelligence (AI), presenting the possibility of self-powered operation when paired …

The development of memristors operating at low switching voltages< 50 mV can be very
useful to avoid signal amplification in many types of circuits, such as those used in …

With the emergence of neuromorphic hardware as a promising low-power parallel
computing platform, the need for tools that allow researchers and engineers to efficiently …