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Programming Protocol Optimization for Analog Weight Tuning in Resistive Memories

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Investigation of statistical retention of filamentary analog RRAM for neuromophic computing

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Resistive Memory-Based Analog Synapse: The Pursuit for Linear and Symmetric Weight Update

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Characterizing Endurance Degradation of Incremental Switching in Analog RRAM for Neuromorphic Systems

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Parallel Architecture With Resistive Crosspoint Array for Dictionary Learning Acceleration

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Stochastic Data-driven Hardware Resilience to Efficiently Train Inference Models for Stochastic Hardware Implementations

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Recent Advances and Future Prospects for Memristive Materials, Devices, and Systems

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Resistive Crossbars as Approximate Hardware Building Blocks for Machine Learning: Opportunities and Challenges

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Metaplastic and energy-efficient biocompatible graphene artificial synaptic transistors for enhanced accuracy neuromorphic computing

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In-Memory Computing in Emerging Memory Technologies for Machine Learning: An Overview

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Compute-in-Memory with Emerging Nonvolatile-Memories: Challenges and Prospects

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MemTorch: An Open-source Simulation Framework for Memristive Deep Learning Systems

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A 40-nm MLC-RRAM Compute-in-Memory Macro With Sparsity Control, On-Chip Write-Verify, and Temperature-Independent ADC References

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A domain wall-magnetic tunnel junction artificial synapse with notched geometry for accurate and efficient training of deep neural networks

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HfO x /AlO y Superlattice‐Like Memristive Synapse

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A comprehensive review of advanced trends: from artificial synapses to neuromorphic systems with consideration of non-ideal effects

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Synaptic Transistor Based on In‐Ga‐Zn‐O Channel and Trap Layers with Highly Linear Conductance Modulation for Neuromorphic Computing

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AND Flash Array Based on Charge Trap Flash for Implementation of Convolutional Neural Networks

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On-Chip Training Spiking Neural Networks Using Approximated Backpropagation With Analog Synaptic Devices

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Flexible Low‐Voltage MXene Floating‐Gate Synaptic Transistor for Neuromorphic Computing and Cognitive Learning

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In situ Parallel Training of Analog Neural Network Using Electrochemical Random-Access Memory

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Long Duration Persistent Photocurrent in 3 nm Thin Doped Indium Oxide for Integrated Light Sensing and In‐Sensor Neuromorphic Computation

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Ferroelectric devices and circuits for neuro-inspired computing

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Demonstration of Synaptic Behavior in a Heavy-Metal-Ferromagnetic-Metal-Oxide-Heterostructure-Based Spintronic Device for On-Chip Learning in Crossbar-Array-Based Neural Networks

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Array-Level Programming of 3-Bit per Cell Resistive Memory and Its Application for Deep Neural Network Inference

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Combined HW/SW Drift and Variability Mitigation for PCM-Based Analog In-Memory Computing for Neural Network Applications

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Efficient and Robust RRAM-Based Convolutional Weight Mapping With Shifted and Duplicated Kernel

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A Non-Idealities Aware Software–Hardware Co-Design Framework for Edge-AI Deep Neural Network Implemented on Memristive Crossbar

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Li-Ion Doped Artificial Synaptic Memristor for Highly Linear Neuromorphic Computing

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Heterogeneous 3-D Integration of Multitier Compute-in-Memory Accelerators: An Electrical-Thermal Co-Design

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Realizing In-Memory Baseband Processing for Ultrafast and Energy-Efficient 6G

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NEAT: Nonlinearity Aware Training for Accurate, Energy-Efficient, and Robust Implementation of Neural Networks on 1T-1R Crossbars

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Effect of Program Error in Memristive Neural Network With Weight Quantization

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Ferroelectric Field-Effect Transistors for Binary Neural Network With 3-D NAND Architecture

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Neuromorphic Computing with Emerging Antiferromagnetic Ordering in Spin–Orbit Torque Devices

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High‐Performance Synapse Arrays for Neuromorphic Computing via Floating Gate‐Engineered IGZO Synaptic Transistors

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Parasitic-Aware Modeling and Neural Network Training Scheme for Energy-Efficient Processing-in-Memory With Resistive Crossbar Array

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A Survey of Memory-Centric Energy Efficient Computer Architecture

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MINT: Mixed-Precision RRAM-Based IN-Memory Training Architecture

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Magnetic skyrmions and domain walls for logical and neuromorphic computing

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Pruning of Deep Neural Networks for Fault-Tolerant Memristor-based Accelerators

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Investigating Ferroelectric Minor Loop Dynamics and History Effect—Part II: Physical Modeling and Impact on Neural Network Training

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Modeling and simulating in-memory memristive deep learning systems: An overview of current efforts

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Investigation of Low-Frequency Noise Characteristics in Gated Schottky Diodes

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Robust RRAM-based In-Memory Computing in Light of Model Stability

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Memristor‐Based Biologically Plausible Memory Based on Discrete and Continuous Attractor Networks for Neuromorphic Systems

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Highly Linear and Symmetric Analog Neuromorphic Synapse Based on Metal Oxide Semiconductor Transistors with Self‐Assembled Monolayer for High‐Precision Neural Network Computation

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Framework for In-Memory Computing Based on Memristor and Memcapacitor for On-Chip Training

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Contact‐Engineered Oxide Memtransistors for Homeostasis‐Based High‐Linearity and Precision Neuromorphic Computing

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Electrical Analysis of Atomic Layer Deposited Thin HfO2 and HfO2/Ta2O5-Based Memristive Devices

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Empirical metal-oxide RRAM device endurance and retention model for deep learning simulations

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Hf0.5Zr0.5O2-Based Ferroelectric Tunnel Junction as an Artificial Synapse for Speech Recognition

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Efficient Processing of MLPerf Mobile Workloads Using Digital Compute-In-Memory Macros

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Metaplasticity‐Enabled Graphene Quantum Dot Devices for Mitigating Catastrophic Forgetting in Artificial Neural Networks

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Enhancing LiAlO X synaptic performance by reducing the Schottky barrier height for deep neural network applications

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All-Spin Artificial Neural Network Based on Spin–Orbit Torque-Induced Magnetization Switching

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Integrated Artificial Neural Network with Trainable Activation Function Enabled by Topological Insulator-Based Spin–Orbit Torque Devices

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When in-memory computing meets spiking neural networks—A perspective on device-circuit-system-and-algorithm co-design

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Overcoming Challenges for Achieving High in-situ Training Accuracy with Emerging Memories

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On-line Functional Testing of Memristor-mapped Deep Neural Networks using Backdoored Checksums

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VWC-SDK: Convolutional Weight Mapping Using Shifted and Duplicated Kernel With Variable Windows and Channels

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Vertically Stacked Nanosheet FET: Charge- Trapping Memory and Synapse With Linear Weight Adjustability for Neuromorphic Computing Applications

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Characterization and Mitigation of IR-Drop in RRAM-based Compute In-Memory

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Graphene-Based Artificial Dendrites for Bio-Inspired Learning in Spiking Neuromorphic Systems

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Edge PoolFormer: Modeling and Training of PoolFormer Network on RRAM Crossbar for Edge-AI Applications

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Temporal State Machines: Using Temporal Memory to Stitch Time-based Graph Computations

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Efficient Identification of Critical Faults in Memristor-Based Inferencing Accelerators

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Two-dimensional materials for artificial synapses: toward a practical application

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Optimized Programming Scheme Enabling Symmetric Conductance Modulation in HfO₂ Resistive Random-Access Memory (RRAM) for Neuromorphic Systems

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HyDe: A Hybrid PCM/FeFET/SRAM Device-Search for Optimizing Area and Energy-Efficiencies in Analog IMC Platforms

Bhattacharjee, A., Moitra, A., & Panda, P. (2023) · IEEE Journal on Emerging and Selected Topics in Circuits and Systems · doi:10.1109/jetcas.2023.3327748 · Cited by 12 · see citing works

Toward Optimized In‐Memory Reinforcement Learning: Leveraging 1/f Noise of Synaptic Ferroelectric Field‐Effect‐Transistors for Efficient Exploration

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Examining the Role and Limits of Batchnorm Optimization to Mitigate Diverse Hardware-noise in In-memory Computing

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RRAM-PoolFormer: A Resistive Memristor-based PoolFormer Modeling and Training Framework for Edge-AI Applications

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Are SNNs Truly Energy-efficient? — A Hardware Perspective

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Efficient Identification of Critical Faults in Memristor Crossbars for Deep Neural Networks

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D-NAT: Data-Driven Non-Ideality Aware Training Framework for Fabricated Computing-In-Memory Macros

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A Knowledge Distillation Online Training Circuit for Fault Tolerance in Memristor Crossbar Array-Based Neural Networks

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Effect of Proton Irradiation Fluence on the Linearity and Symmetry of Conductance Tuning of a HfOx/TiOx Heterojunction-Based Memristor

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Benchmark Non-volatile and Volatile Memory Based Hybrid Precision Synapses for In-situ Deep Neural Network Training

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Bidirectional Analog Conductance Modulation for RRAM-Based Neural Networks

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High Performance DUV Synaptic Oxide Thin Film Transistor with Rapid Response

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Synaptic Transistors Using Scalable Graphene Nanoribbons

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All‐Electrical Control of Spin Synapses for Neuromorphic Computing: Bridging Multi‐State Memory with Quantization for Efficient Neural Networks

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Organic Electrochemical Random Access Memory: From Bio‐Inspired to Bio‐Integrated

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Optimization of post-annealing temperature for high-performance synaptic transistors based on In–Ga–Zn–O channel and trap layers

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Utilizing Dual-Port FeFETs for Energy-Efficient Binary Neural Network Inference Accelerators

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ClipFormer : Key–Value Clipping of Transformers on Memristive Crossbars for Write Noise Mitigation

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Design Exploration of ReRAM-Based Crossbar for AI Inference

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Untitled work

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COMPUTE-IN-MEMORY WITH EMERGING NON-VOLATILE MEMORIES FOR ACCELERATING DEEP NEURAL NETWORKS

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Exploring the Impact of Variability in Resistance Distributions of RRAM on the Prediction Accuracy of Deep Learning Neural Networks

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