Deep Neural Networks (DNNs) have achieved great progress in academia and industry. But
they have become computational and memory intensive with the increase of network depth …

Conventional in-memory computing (IMC) architectures consist of analog memristive
crossbars to accelerate matrix-vector multiplication (MVM), and digital functional units to …

Artificial neural networks (ANNs) have recently made a significant impact on the field of
industrial electronics. Increasing efforts have been focused on implementing ANN using …

Tensor processing units (TPUs), specialized hardware accelerators for machine learning
tasks, have shown significant performance improvements when executing convolutional …

Nonlinear activation functions play a crucial role in artificial neural networks. However,
digital implementations of sigmoidal functions, the commonly used activation functions, are …

With the increased attention to memristive-based in-memory analog computing (IMAC)
architectures as an alternative for energy-hungry computer systems for machine learning …

Convolutional neural networks (CNNs) offer potentially a better accuracy alternative for
conventional deep learning tasks. The hardware implementation of CNN functionalities with …

Given the growing focus on memristive crossbar-based in-memory computing (IMC)
architectures as a potential alternative to current energy-hungry machine learning hardware …

In recent years, spintronic devices and non-Von Neuman architectures have emerged as
two promising approaches to overcome the power, performance, and efficiency limitations of …

The research tends to suggest a spin-orbit torque magnetic random access memory (SOT-
MRAM)-based Binary CNN In-Memory Accelerator (BIMA) to minimize power utilization and …