Results 51 to 60 of about 5,289 (241)
Unsupervised Learning on Resistive Memory Array Based Spiking Neural Networks
Frontiers in Neuroscience, 2019 Spiking Neural Networks (SNNs) offer great potential to promote both the performance and efficiency of real-world computing systems, considering the biological plausibility of SNNs.
Yilong Guo +3 more
doaj +1 more source
Nanoscale Research Letters, 2020 In this manuscript, recent progress in the area of resistive random access memory (RRAM) technology which is considered one of the most standout emerging memory technologies owing to its high speed, low cost, enhanced storage density, potential ...
Furqan Zahoor +2 more
doaj +1 more source
Modeling the physics of RRAM defects: A model simulating RRAM defects on a macroscopic physical level [PDF]
, 2020 Resistive RAM, or RRAM, is one of the emerging non-volatile memory (NVM) technologies, which could be used in the near future to fill the gap in the memory hierarchy between dynamic RAM (DRAM) and Flash, or even completely replace Flash.
Hol, Tijs (author)
core
Accurate Inference With Inaccurate RRAM Devices: A Joint Algorithm-Design Solution
IEEE Journal on Exploratory Solid-State Computational Devices and Circuits, 2020 Resistive random access memory (RRAM) is a promising technology for energy-efficient neuromorphic accelerators. However, when a pretrained deep neural network (DNN) model is programmed to an RRAM array for inference, the model suffers from accuracy ...
Gouranga Charan +5 more
doaj +1 more source
Nanomaterials, 2023 In this study, the bipolar switching properties and electrical conduction behaviors of the ITO thin films RRAM devices were investigated. For the transparent RRAM devices structure, indium tin oxide thin films were deposited by using the RF magnetron ...
Kai-Huang Chen +3 more
doaj +1 more source
A novel peripheral circuit for RRAM-based LUT [PDF]
, 2012 Resistive random access memory (RRAM) is a promising candidate to substitute static random access memory (SRAM) in lookup table (LUT) design for its high density and non-volatility.
Wei Zhang +6 more
core +1 more source
RRAM Crossbar-Based Fault-Tolerant Binary Neural Networks (BNNs) [PDF]
, 2022 Computation-In Memory (CIM) using RRAM crossbar array is a promising solution to realize energy-efficient neuromorphic hardware, such as Binary Neural Networks (BNNs). However, RRAM faults restrict the applicability of CIM for BNN implementation.
Hamdioui, S. (author) +2 more
core +1 more source
POS1 - RRAM Crossbar-Based Fault-Tolerant Binary Neural Networks (BNNs) [PDF]
, 2022 Computation-In Memory (CIM) using RRAM crossbar array is a promising solution to realize energy-efficient neuromorphic hardware, such as Binary Neural Networks (BNNs). However, RRAM faults restrict the applicability of CIM for BNN implementation.
Zografou, Artemis +2 more
core +2 more sources
Neuromorphic Computing and Engineering, 2023 The long-time retention issue of resistive random access memory (RRAM) brings a great challenge in the performance maintenance of large-scale RRAM-based computation-in-memory (CIM) systems.
Yibei Zhang +9 more
doaj +1 more source
RRAM based neuromorphic algorithms
CoRR, 2019 This submission is a report on RRAM based neuromorphic algorithms. This report basically gives an overview of the algorithms implemented on neuromorphic hardware with crossbar array of RRAM synapses. This report mainly talks about the work on deep neural network to spiking neural network conversion and its significance.
openaire +2 more sources