A comprehensive review of advanced trends: from artificial synapses to neuromorphic systems with consideration of non-ideal effects
A neuromorphic system is composed of hardware-based artificial neurons and synaptic
devices, designed to improve the efficiency of neural computations inspired by energy …
devices, designed to improve the efficiency of neural computations inspired by energy …
Layer ensemble averaging for fault tolerance in memristive neural networks
O Yousuf, BD Hoskins, K Ramu, M Fream… - Nature …, 2025 - nature.com
Artificial neural networks have advanced due to scaling dimensions, but conventional
computing struggles with inefficiencies due to memory bottlenecks. In-memory computing …
computing struggles with inefficiencies due to memory bottlenecks. In-memory computing …
Experimental assessment of multilevel rram-based vector-matrix multiplication operations for in-memory computing
EPB Quesada, MK Mahadevaiah… - … on Electron Devices, 2023 - ieeexplore.ieee.org
Resistive random access memory (RRAM)-based hardware accelerators are playing an
important role in the implementation of in-memory computing (IMC) systems for artificial …
important role in the implementation of in-memory computing (IMC) systems for artificial …
Layer Ensemble Averaging for Improving Memristor-Based Artificial Neural Network Performance
The Lynchpin of In-Memory Computing: A Benchmarking Framework for Vector-Matrix Multiplication in RRAMs
MTR Chowdhury, HQN Vo, P Ramanan… - 2024 International …, 2024 - ieeexplore.ieee.org
The Von Neumann bottleneck, a fundamental challenge in conventional computer
architecture, arises from the inability to execute fetch and data operations simultaneously …
architecture, arises from the inability to execute fetch and data operations simultaneously …
A CMOS Analog Neuron Circuit with A Multi-Level Memory
MD Edwards, NJ Sarhan… - … on Microelectronics (ICM), 2023 - ieeexplore.ieee.org
This paper presents a CMOS-based analog neuron circuit that utilizes a multi-level analog
memory that is useful for mixed signal neural networks. The implementation of neural …
memory that is useful for mixed signal neural networks. The implementation of neural …
[HTML][HTML] Analysis of VMM computation strategies to implement BNN applications on RRAM arrays
The growing interest in edge-AI solutions and advances in the field of quantized neural
networks have led to hardware efficient binary neural networks (BNNs). Extreme BNNs …
networks have led to hardware efficient binary neural networks (BNNs). Extreme BNNs …
A failure analysis framework of ReRAM In-Memory Logic operations
L Brackmann, A Jafari, C Bengel… - … Test Conference in …, 2022 - ieeexplore.ieee.org
Computation-in-Memory (CiM) with emerging non-volatile memories leads to significant
performance and energy efficiency, which is a promising approach to address so-called …
performance and energy efficiency, which is a promising approach to address so-called …