Analog in-memory computing is a promising future technology for efficiently accelerating
deep learning networks. While using in-memory computing to accelerate the inference …

The recent co-optimization of memristive technologies and programming algorithms enabled
neural networks training with in-memory computing systems. In this context, novel analog …

The computational cost of AI could be alleviated by accelerating the synaptic transfer
calculations in artificial neural networks with an analog crossbar array processor. In this …

Analog in-memory computing with resistive memory devices is a compelling alternative to
conventional digital von Neumann computers [1]. Recent advancements in learning …

In this chapter, memristor technology and a crossbar array platform play crucial roles in
realizing the analog AI hardware. The memristor devices store synaptic weights as …

Over the last decade, standard computing architectures based on von Neumann paradigm
struggled to manage Internet of Things and Artificial Intelligence (AI) workloads. The …

We pioneer a physics based compact model of analog filamentary conductive-metal-oxide
(CMO)/HfO x ReRAM devices. Drawing from established physics-based models, we extend …

Nonlinear optical extreme learning machines (ELMs) leverage the optics-owned dynamics
of nonlinear wave propagation in complex media for neuromorphic computing without the …