The growing adoption of data-driven applications, such as artificial intelligence (AI), is
transforming the way we interact with technology. Currently, the deployment of AI and …

Specialized accelerators have recently garnered attention as a method to reduce the power
consumption of neural network inference. A promising category of accelerators utilizes …

With the increasing demands in modern communications, the concepts of energy-efficient
and low-cost processors have received a lot of attention from researchers in recent years …

The Abisko project aims to develop an energy-efficient spiking neural network (SNN)
computing architecture and software system capable of autonomous learning and operation …

A brain-like neuromorphic computing system, as compared with traditional Von Neumann
architecture, has broad application prospects in the fields of emerging artificial intelligence …

Computation-in-Memory accelerators based on resistive switching devices represent a
promising approach to realize future information processing systems. These architectures …

In recent years, digital computing in memory (CIM) has been an efficient and high-
performance solution in artificial intelligence (AI) edge inference. Nevertheless, digital CIM …

The analog-to-digital converter (ADC) is not only a key component in analog in-memory
computing (IMC) accelerators but also a bottleneck for the efficiency and accuracy of these …

There is a growing market for technologies ded-icated to accelerating Artificial Intelligence
(AI) workloads. Many of these emerging architectures promise to provide savings in energy …

In-memory computing (IMC) architectures mitigate the von Neumann bottleneck
encountered in traditional deep learning accelerators. Its energy efficiency can realize deep …