Memory devices and applications for in-memory computing
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 …
units. However, data movement is costly in terms of time and energy and this problem is …
Towards spike-based machine intelligence with neuromorphic computing
Guided by brain-like 'spiking'computational frameworks, neuromorphic computing—brain-
inspired computing for machine intelligence—promises to realize artificial intelligence while …
inspired computing for machine intelligence—promises to realize artificial intelligence while …
Resistive switching materials for information processing
The rapid increase in information in the big-data era calls for changes to information-
processing paradigms, which, in turn, demand new circuit-building blocks to overcome the …
processing paradigms, which, in turn, demand new circuit-building blocks to overcome the …
Ferroelectric field-effect transistors based on HfO2: a review
In this article, we review the recent progress of ferroelectric field-effect transistors (FeFETs)
based on ferroelectric hafnium oxide (HfO 2), ten years after the first report on such a device …
based on ferroelectric hafnium oxide (HfO 2), ten years after the first report on such a device …
All-optical spiking neurosynaptic networks with self-learning capabilities
Software implementations of brain-inspired computing underlie many important
computational tasks, from image processing to speech recognition, artificial intelligence and …
computational tasks, from image processing to speech recognition, artificial intelligence and …
Bridging biological and artificial neural networks with emerging neuromorphic devices: fundamentals, progress, and challenges
As the research on artificial intelligence booms, there is broad interest in brain‐inspired
computing using novel neuromorphic devices. The potential of various emerging materials …
computing using novel neuromorphic devices. The potential of various emerging materials …
An overview of phase-change memory device physics
Phase-change memory (PCM) is an emerging non-volatile memory technology that has
recently been commercialized as storage-class memory in a computer system. PCM is also …
recently been commercialized as storage-class memory in a computer system. PCM is also …
Hardware-aware training for large-scale and diverse deep learning inference workloads using in-memory computing-based accelerators
Analog in-memory computing—a promising approach for energy-efficient acceleration of
deep learning workloads—computes matrix-vector multiplications but only approximately …
deep learning workloads—computes matrix-vector multiplications but only approximately …
Memristor modeling: challenges in theories, simulations, and device variability
This article presents a review of the current development and challenges in memristor
modeling. We review the mechanisms of memristive devices based on various …
modeling. We review the mechanisms of memristive devices based on various …
Accurate deep neural network inference using computational phase-change memory
In-memory computing using resistive memory devices is a promising non-von Neumann
approach for making energy-efficient deep learning inference hardware. However, due to …
approach for making energy-efficient deep learning inference hardware. However, due to …