Neural networks have enabled applications in artificial intelligence through machine
learning, and neuromorphic computing. Software implementations of neural networks on …

As data movement operations and power-budget become key bottlenecks in the design of
computing systems, the interest in unconventional approaches such as processing-in …

Memristor crossbars offer reconfigurable non-volatile resistance states and could remove
the speed and energy efficiency bottleneck in vector-matrix multiplication, a core computing …

Vector-matrix multiplication dominates the computation time and energy for many workloads,
particularly neural network algorithms and linear transforms (eg, the Discrete Fourier …

Analogue in-memory computing using memristors could alleviate the performance
constraints imposed by digital von Neumann systems in data-intensive tasks. Conventional …

Memristive devices are two-terminal devices that can change their resistance state upon
application of appropriate voltage stimuli. The resistance can be tuned over a wide …

Conventional digital computers can execute advanced operations by a sequence of
elementary Boolean functions of 2 or more bits. As a result, complicated tasks such as …

Exploiting model sparsity to reduce ineffectual computation is a commonly used approach to
achieve energy efficiency for DNN inference accelerators. However, due to the tightly …

The cross-point array architecture with resistive synaptic devices has been proposed for on-
chip implementation of weighted sum and weight update in the training process of learning …

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 …