The discovery of ferroelectricity in oxides that are compatible with modern semiconductor
manufacturing processes, such as hafnium oxide, has led to a re-emergence of the …

Compute-in-memory (CIM) is a new computing paradigm that addresses the memory-wall
problem in hardware accelerator design for deep learning. The input vector and weight …

The growing computational demand in artificial intelligence calls for hardware solutions that
are capable of in situ machine learning, where both training and inference are performed by …

The rapid development of artificial intelligence (AI) demands the rapid development of
domain-specific hardware specifically designed for AI applications. Neuro-inspired …

Brain-inspired parallel computing, which is typically performed using a hardware neural-
network platform consisting of numerous artificial synapses, is a promising technology for …

Two-terminal resistive switching devices are commonly plagued with longstanding scientific
issues including interdevice variability and sneak current that lead to computational errors …

Emerging non-volatile memory (eNVM) based resistive synaptic devices have shown great
potential for implementing deep neural networks (DNNs). However, the eNVM devices …

The shift towards a distributed computing paradigm, where multiple systems acquire and
elaborate data in real-time, leads to challenges that must be met. In particular, it is becoming …

Hyperdimensional computing (HDC) is a brain-inspired computational framework that relies
on long hypervectors (HVs) for learning. In HDC, computational operations consist of simple …

The Internet of Things (IoT) is an interface with the physical world that usually operates in
random-sparse-event (RSE) scenarios. This article discusses main challenges of IoT chips …