Soft and flexible: core-shell ionic liquid resistive memory for electronic synapses [PDF]
Microsystems & Nanoengineering, 2021 The human brain is the most efficient computational and intelligent system, and researchers are trying to mimic the human brain using solid-state materials.
Muhammad Umair Khan +5 more
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Ratio-based multi-level resistive memory cells [PDF]
Scientific Reports, 2021 Ratio-based encoding has recently been proposed for single-level resistive memory cells, in which the resistance ratio of a pair of resistance-switching devices, rather than the resistance of a single device (i.e.
Miguel Angel Lastras-Montaño +5 more
doaj +3 more sources
Recent advances in organic‐based materials for resistive memory applications
InfoMat, 2020 With the rapid development of data‐driven human interaction, advanced data‐storage technologies with lower power consumption, larger storage capacity, faster switching speed, and higher integration density have become the goals of future memory ...
Yang Li +9 more
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Full hardware implementation of neuromorphic visual system based on multimodal optoelectronic resistive memory arrays for versatile image processing. [PDF]
Nat Commun, 2023 In-sensor and near-sensor computing are becoming the next-generation computing paradigm for high-density and low-power sensory processing. To fulfil a high-density and efficient neuromorphic visual system with fully hierarchical emulation of the retina ...
Zhou G +12 more
europepmc +2 more sources
Self-rectifying resistive memory in passive crossbar arrays. [PDF]
Nat Commun, 2021 Conventional computing architectures are poor suited to the unique workload demands of deep learning, which has led to a surge in interest in memory-centric computing.
Jeon K +7 more
europepmc +2 more sources
Wafer-scale solution-processed 2D material analog resistive memory array for memory-based computing. [PDF]
Nat Commun, 2022 Realization of high-density and reliable resistive random access memories based on two-dimensional semiconductors is crucial toward their development in next-generation information storage and neuromorphic computing.
Tang B +10 more
europepmc +2 more sources
In-Memory Computing with Resistive Memory Circuits: Status and Outlook [PDF]
Electronics, 2021 In-memory computing (IMC) refers to non-von Neumann architectures where data are processed in situ within the memory by taking advantage of physical laws. Among the memory devices that have been considered for IMC, the resistive switching memory (RRAM), also known as memristor, is one of the most promising technologies due to its relatively easy ...
Pedretti G., Ielmini D.
openaire +2 more sources
Ex Situ Transfer of Bayesian Neural Networks to Resistive Memory‐Based Inference Hardware
Advanced Intelligent Systems, 2021 Neural networks cannot typically be trained locally in edge‐computing systems due to severe energy constraints. It has, therefore, become commonplace to train them “ex situ” and transfer the resulting model to a dedicated inference hardware.
Thomas Dalgaty +4 more
doaj +2 more sources
One-step regression and classification with cross-point resistive memory arrays. [PDF]
Sci Adv, 2020 Machine learning algorithms such as linear regression are trained in one step with cross-point resistive memory arrays. Machine learning has been getting attention in recent years as a tool to process big data generated by the ubiquitous sensors used in ...
Sun Z +3 more
europepmc +3 more sources
Pruning random resistive memory for optimizing analog AI [PDF]
Nature CommunicationsThe rapid expansion of AI models has intensified concerns over energy consumption. Analog in-memory computing with resistive memory offers a promising, energy-efficient alternative, yet its practical deployment is hindered by programming challenges and ...
Yi Li +22 more
doaj +2 more sources