Hardware implementation of memristor-based artificial neural networks
Artificial Intelligence (AI) is currently experiencing a bloom driven by deep learning (DL)
techniques, which rely on networks of connected simple computing units operating in …
techniques, which rely on networks of connected simple computing units operating in …
Neural architecture search for in-memory computing-based deep learning accelerators
The rapid growth of artificial intelligence and the increasing complexity of neural network
models are driving demand for efficient hardware architectures that can address power …
models are driving demand for efficient hardware architectures that can address power …
SIAM: Chiplet-based scalable in-memory acceleration with mesh for deep neural networks
In-memory computing (IMC) on a monolithic chip for deep learning faces dramatic
challenges on area, yield, and on-chip interconnection cost due to the ever-increasing …
challenges on area, yield, and on-chip interconnection cost due to the ever-increasing …
A survey of neuromorphic computing-in-memory: Architectures, simulators, and security
This work is a survey of neuromorphic computing-in-memory. Unlike existing surveys that
focus on hardware or application-level perspectives, the authors elaborate on architectures …
focus on hardware or application-level perspectives, the authors elaborate on architectures …
TxSim: Modeling training of deep neural networks on resistive crossbar systems
Deep neural networks (DNNs) have gained tremendous popularity in recent years due to
their ability to achieve superhuman accuracy in a wide variety of machine learning tasks …
their ability to achieve superhuman accuracy in a wide variety of machine learning tasks …
Architecture-circuit-technology co-optimization for resistive random access memory-based computation-in-memory chips
Abstract Computation-in-memory (CIM) chips offer an energy-efficient approach to artificial
intelligence computing workloads. Resistive random-access memory (RRAM)-based CIM …
intelligence computing workloads. Resistive random-access memory (RRAM)-based CIM …
Impact of on-chip interconnect on in-memory acceleration of deep neural networks
With the widespread use of Deep Neural Networks (DNNs), machine learning algorithms
have evolved in two diverse directions—one with ever-increasing connection density for …
have evolved in two diverse directions—one with ever-increasing connection density for …
Framework for in-memory computing based on memristor and memcapacitor for on-chip training
A Singh, BG Lee - IEEE Access, 2023 - ieeexplore.ieee.org
Memristive crossbar arrays have gained considerable attention from researchers to perform
analog in-memory vector-matrix multiplications in machine learning accelerators with low …
analog in-memory vector-matrix multiplications in machine learning accelerators with low …
Review of security techniques for memristor computing systems
Neural network (NN) algorithms have become the dominant tool in visual object recognition,
natural language processing, and robotics. To enhance the computational efficiency of these …
natural language processing, and robotics. To enhance the computational efficiency of these …
A flexible and fast digital twin for RRAM systems applied for training resilient neural networks
Abstract Resistive Random Access Memory (RRAM) has gained considerable momentum
due to its non-volatility and energy efficiency. Material and device scientists have been …
due to its non-volatility and energy efficiency. Material and device scientists have been …