Data scarcity is a major challenge when training deep learning (DL) models. DL demands a
large amount of data to achieve exceptional performance. Unfortunately, many applications …

Solving computationally hard problems using conventional computing architectures is often
slow and energetically inefficient. Quantum computing may help with these challenges, but it …

The coherent Ising machine (CIM) is designed to solve the NP-hard Ising problem quickly
and energy efficiently. Boolean satisfiability (SAT) and maximum satisfiability (Max-SAT) are …

Physical neural networks are artificial neural networks that mimic synapses and neurons
using physical systems or materials. These networks harness the distinctive characteristics …

The transistor celebrated its 75th birthday in 2022. The continued scaling of the transistor
defined by Moore's law continues, albeit at a slower pace. Meanwhile, computing demands …

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 …

The conventional computing method based on the von Neumann architecture is limited by a
series of problems such as high energy consumption, finite data exchange bandwidth …

Recently, there has been increasing activity to build dedicated Ising machines to accelerate
the solution of combinatorial optimization problems by expressing these problems as a …

Artificial intelligence has become indispensable in modern life, but its energy consumption
has become a significant concern due to its huge storage and computational demands …

Probabilistic computing has been introduced to operate functional networks using a
probabilistic bit (p-bit), broadening the computational abilities in non-deterministic …