Memristive arrays are a natural fit to implement spiking neural network (SNN) acceleration.
Representing information as digital spiking events can improve noise margins and tolerance …

Memristors have attracted increasing attention due to their tremendous potential to
accelerate data-centric computing systems. The dynamic reconfiguration of memristive …

Compute-in-Memory (CIM) implemented with Resistive-Random-Access-Memory (RRAM)
crossbars is a promising approach for Deep Neural Network (DNN) acceleration. As the …

Abstract Compute-in-memory (CIM) accelerators based on emerging memory devices are of
potential use in edge artificial intelligence and machine learning applications due to their …

As more cloud computing resources are used for machine learning training and inference
processes, privacy-preserving techniques that protect data from revealing at the cloud …

Network features found in the brain may help implement more efficient and robust neural
networks. Spiking neural networks (SNNs) process spikes in the spatiotemporal domain and …

Deep learning accelerators (DLAs) based on compute-in-memory (CIM) technologies have
been considered promising candidates to drastically improve the throughput and energy …

Noise in image sensors led to the development of a whole range of denoising filters. A noisy
image can become hard to recognize and often require several types of post-processing …

A memristor array has emerged as a potential computing hardware for artificial intelligence
(AI). It has an inherent memory effect that allows information storage in the form of easily …

Deep neural network (DNN) accelerators employing crossbar arrays capable of in-memory
computing (IMC) are highly promising for neural computing platforms. However, in deeply …