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 …
Memristive crossbar arrays for storage and computing applications
The emergence of memristors with potential applications in data storage and artificial
intelligence has attracted wide attentions. Memristors are assembled in crossbar arrays with …
intelligence has attracted wide attentions. Memristors are assembled in crossbar arrays with …
A 64-core mixed-signal in-memory compute chip based on phase-change memory for deep neural network inference
Analogue in-memory computing (AIMC) with resistive memory devices could reduce the
latency and energy consumption of deep neural network inference tasks by directly …
latency and energy consumption of deep neural network inference tasks by directly …
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 …
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 …
[HTML][HTML] Analog architectures for neural network acceleration based on non-volatile memory
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 …
have the potential to overcome the major bottlenecks faced by digital hardware for data …
Advances in memristor-based neural networks
W Xu, J Wang, X Yan - Frontiers in Nanotechnology, 2021 - frontiersin.org
The rapid development of artificial intelligence (AI), big data analytics, cloud computing, and
Internet of Things applications expect the emerging memristor devices and their hardware …
Internet of Things applications expect the emerging memristor devices and their hardware …
A flexible and fast PyTorch toolkit for simulating training and inference on analog crossbar arrays
We introduce the IBM ANALOG HARDWARE ACCELERATION KIT, a new and first of a kind
open source toolkit to simulate analog crossbar arrays in a convenient fashion from within …
open source toolkit to simulate analog crossbar arrays in a convenient fashion from within …
Bringing uncertainty quantification to the extreme-edge with memristor-based Bayesian neural networks
Safety-critical sensory applications, like medical diagnosis, demand accurate decisions from
limited, noisy data. Bayesian neural networks excel at such tasks, offering predictive …
limited, noisy data. Bayesian neural networks excel at such tasks, offering predictive …
Multiply accumulate operations in memristor crossbar arrays for analog computing
J Chen, J Li, Y Li, X Miao - Journal of Semiconductors, 2021 - iopscience.iop.org
Memristors are now becoming a prominent candidate to serve as the building blocks of non-
von Neumann in-memory computing architectures. By mapping analog numerical matrices …
von Neumann in-memory computing architectures. By mapping analog numerical matrices …