Compute in‐memory with non‐volatile elements for neural networks: A review from a co‐design perspective
W Haensch, A Raghunathan, K Roy… - Advanced …, 2023 - Wiley Online Library
Deep learning has become ubiquitous, touching daily lives across the globe. Today,
traditional computer architectures are stressed to their limits in efficiently executing the …
traditional computer architectures are stressed to their limits in efficiently executing the …
Role of oxygen vacancies in ferroelectric or resistive switching hafnium oxide
HfO2 shows promise for emerging ferroelectric and resistive switching (RS) memory devices
owing to its excellent electrical properties and compatibility with complementary metal oxide …
owing to its excellent electrical properties and compatibility with complementary metal oxide …
Concealable physically unclonable function chip with a memristor array
A physically unclonable function (PUF) is a creditable and lightweight solution to the mistrust
in billions of Internet of Things devices. Because of this remarkable importance, PUF need to …
in billions of Internet of Things devices. Because of this remarkable importance, PUF need to …
Binary convolutional neural network on RRAM
Recent progress in the machine learning field makes low bit-level Convolutional Neural
Networks (CNNs), even CNNs with binary weights and binary neurons, achieve satisfying …
Networks (CNNs), even CNNs with binary weights and binary neurons, achieve satisfying …
Technological exploration of RRAM crossbar array for matrix-vector multiplication
Matrix-vector multiplication is the key operation for many computationally intensive
algorithms. The emerging metal oxide resistive switching random access memory (RRAM) …
algorithms. The emerging metal oxide resistive switching random access memory (RRAM) …
Fault-tolerant training with on-line fault detection for RRAM-based neural computing systems
An RRAM-based computing system (RCS) is an attractive hardware platform for
implementing neural computing algorithms. Online training for RCS enables hardware …
implementing neural computing algorithms. Online training for RCS enables hardware …
Stuck-at fault tolerance in RRAM computing systems
Emerging metal-oxide resistive switching random-access memory (RRAM) devices and
RRAM crossbars have demonstrated their potential in boosting the speed and energy …
RRAM crossbars have demonstrated their potential in boosting the speed and energy …
Improved Switching Stability and the Effect of an Internal Series Resistor in HfO2/TiOx Bilayer ReRAM Cells
A Hardtdegen, C La Torre, F Cüppers… - IEEE transactions on …, 2018 - ieeexplore.ieee.org
Bipolar redox-based resistive random-access memory cells are intensively studied for new
storage class memory and beyond von Neumann computing applications. However, the …
storage class memory and beyond von Neumann computing applications. However, the …
Prospect and challenges of analog switching for neuromorphic hardware
To inaugurate energy-efficient hardware as a solution to complex tasks, information
processing paradigms shift from von Neumann to non-von Neumann computing …
processing paradigms shift from von Neumann to non-von Neumann computing …
Forming‐free grain boundary engineered hafnium oxide resistive random access memory devices
A model device based on an epitaxial stack combination of titanium nitride (111) and
monoclinic hafnia (11 1¯) is grown onto ac‐cut Al2O3‐substrate to target the role of grain …
monoclinic hafnia (11 1¯) is grown onto ac‐cut Al2O3‐substrate to target the role of grain …