The use of deep learning (DL) to real-world applications, such as computer vision, speech
recognition, and robotics, has become ubiquitous. This can be largely attributed to a virtuous …

In-memory-computing is emerging as an efficient hardware paradigm for deep neural
network accelerators at the edge, enabling to break the memory wall and exploit massive …

In-memory computing (IMC) using emerging nonvolatile devices has received considerable
attention due to its great potential for accelerating artificial neural networks and machine …

Spiking Neural Networks (SNNs) are bio-plausible models that hold great potential for
realizing energy-efficient implementations of sequential tasks on resource-constrained edge …

This study discusses the feasibility of Ferroelectric Capacitors (FeCaps) and Ferroelectric
Field-Effect Transistors (FeFETs) as In-Memory Computing (IMC) elements to accelerate …

For accelerating Binarized Neural Networks (BNNs), analog computing-based crossbar
accelerators, utilizing XNOR gates and additional interface circuits, have been proposed …

In recent years, Compute-in-memory (CiM) architectures have emerged as a promising
solution for deep neural network (NN) accelerators. Multiply-accumulate (MAC) is …

In-Memory Computing (IMC) has emerged as a promising paradigm for energy-efficient,
throughput-efficient and area-efficient machine learning at the edge. However, the …

In-memory computing (IMC) and quantization have emerged as promising techniques for
edge-based deep neural network (DNN) accelerators by reducing their energy, latency and …

In-memory computing (IMC) with non-volatile memories (NVMs) has emerged as a
promising approach to address the rapidly growing computational demands of Deep Neural …