Analog Synaptic Plasticity in 2D Layered Material Iontronic Memtransistors for Brain-Inspired Computing. [PDF]
Adv Sci (Weinh)In this work, we demonstrated a robust 2D MoS2 based iontronic memtransistor of planar architecture operated under the influence of an electrical double layer with versatile performance and applications, including pinched hysteresis nature of transfer curve, analogue channel conductance tuning, low‐voltage operation, logic‐gate operation, classical ...
Saha P, Bej S, Das BC.
europepmc +2 more sources
Update Disturbance‐Resilient Analog ReRAM Crossbar Arrays for In‐Memory Deep Learning Accelerators [PDF]
Advanced ScienceResistive memory (ReRAM) technologies with crossbar array architectures hold significant potential for analog AI accelerator hardware, enabling both in‐memory inference and training.
Wooseok Choi +16 more
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Exploring resistive switching in flexible, forming-free Ti/NiO/AZO/PET memory device for future wearable electronics [PDF]
Scientific ReportsResistive Random Access Memory (ReRAM) is an emerging class of non-volatile memory that stores data by altering the resistance of a material within a memory cell.
Adiba Adiba +2 more
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NeoHebbian synapses to accelerate online training of neuromorphic hardware [PDF]
Scientific ReportsNeuromorphic systems that employ advanced synaptic learning rules, such as the three-factor learning rule, require synaptic devices of increased complexity.
S. Pande +6 more
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A keyword-based approach to analyzing scientific research trends: ReRAM present and future [PDF]
Scientific ReportsResearch trend analysis is a primary step in defining research structures and predicting research directions from scientific papers. Recently, due to millions of annual scientific publications, researchers demand analytical methods to interpret the ...
Hyeon Kim +5 more
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Implementation of binarized neural networks immune to device variation and voltage drop employing resistive random access memory bridges and capacitive neurons [PDF]
Communications EngineeringResistive Random Access Memories (ReRAM) arrays provides a promising basement to deploy neural network accelerators based on near or in memory computing.
Mona Ezzadeen +12 more
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APL Materials, 2021 The limited sensitivity of existing analysis techniques at the nanometer scale makes it challenging to systematically examine the complex interactions in redox-based resistive random access memory (ReRAM) devices.
Horatio R. J. Cox +7 more
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XMA2: A crossbar-aware multi-task adaption framework via 2-tier masks
Frontiers in Electronics, 2022 Recently, ReRAM crossbar-based deep neural network (DNN) accelerator has been widely investigated. However, most prior works focus on single-task inference due to the high energy consumption of weight reprogramming and ReRAM cells’ low endurance issue ...
Fan Zhang +5 more
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A Data-Driven Verilog-A ReRAM Model [PDF]
IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems, 2018 The translation of emerging application concepts that exploit resistive random access memory (ReRAM) into large-scale practical systems requires realistic yet computationally efficient device models. Here, we present a ReRAM model, where device current–voltage characteristics and resistive switching rate are expressed as a function of: 1) bias voltage ...
Ioannis Messaris +5 more
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Exploiting device-level non-idealities for adversarial attacks on ReRAM-based neural networks
Memories - Materials, Devices, Circuits and Systems, 2023 Resistive memory (ReRAM) or memristor devices offer the prospect of more efficient computing. While memristors have been used for a variety of computing systems, their usage has gained significant popularity in the domain of deep learning.
Tyler McLemore +5 more
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