[HTML][HTML] HfO2-based resistive switching memory devices for neuromorphic computing
HfO 2-based resistive switching memory (RRAM) combines several outstanding properties,
such as high scalability, fast switching speed, low power, compatibility with complementary …
such as high scalability, fast switching speed, low power, compatibility with complementary …
Low conductance state drift characterization and mitigation in resistive switching memories (RRAM) for artificial neural networks
The crossbar structure of Resistive-switching random access memory (RRAM) arrays
enabled the In-Memory Computing circuits paradigm, since they imply the native …
enabled the In-Memory Computing circuits paradigm, since they imply the native …
In-memory computing for machine learning and deep learning
In-memory computing (IMC) aims at executing numerical operations via physical processes,
such as current summation and charge collection, thus accelerating common computing …
such as current summation and charge collection, thus accelerating common computing …
PBA: Percentile-Based Level Allocation for Multiple-Bits-Per-Cell RRAM
Recently, researchers have demonstrated multiple-bits-per-cell (MBPC) data storage using
resistive random access memory (RRAM) device technologies. In MBPC storage, a level …
resistive random access memory (RRAM) device technologies. In MBPC storage, a level …
End-to-end modeling of variability-aware neural networks based on resistive-switching memory arrays
Resistive-switching random access memory (RRAM) is a promising technology that enables
advanced applications in the field of in-memory computing (IMC). By operating the memory …
advanced applications in the field of in-memory computing (IMC). By operating the memory …
Compact modeling of resistive switching memory (RRAM) with voltage and temperature dependences
Resistive-switching memory (RRAM) devices are an attractive technology for in-memory
computing (IMC) to accelerate data-intensive tasks, such as deep neural network (DNN) …
computing (IMC) to accelerate data-intensive tasks, such as deep neural network (DNN) …