Memristive devices, which combine a resistor with memory functions such that voltage
pulses can change their resistance (and hence their memory state) in a nonvolatile manner …

There has been a resurgence of interest in optical computing since the early 2010s, both in
academia and in industry, with much of the excitement centred around special-purpose …

Traditional von Neumann computing systems involve separate processing and memory
units. However, data movement is costly in terms of time and energy and this problem is …

Analog hardware accelerators, which perform computation within a dense memory array,
have the potential to overcome the major bottlenecks faced by digital hardware for data …

As the limits of transistor technology are approached, feature size in integrated circuit
transistors has been reduced very near to the minimum physically-realizable channel length …

Processing In-Memory (PIM) has shown a great potential to accelerate inference tasks of
Convolutional Neural Network (CNN). However, existing PIM architectures do not support …

Memristive devices have been extensively studied for data-intensive tasks such as artificial
neural networks. These types of computing tasks are considered to be 'soft'as they can …

Sparse matrix-vector multiplication (SpMV) is an important primitive across a wide range of
application domains such as scientific computing and graph analytics. Due to its intrinsic …

Duality Cache is an in-cache computation architecture that enables general purpose data
parallel applications to run on caches. This paper presents a holistic approach of building …

As von Neumann computing architectures become increasingly constrained by data-
movement overheads, researchers have started exploring in-memory computing (IMC) …