Results 71 to 80 of about 24,472 (312)

Probabilistic metaplasticity for continual learning with memristors in spiking networks

Scientific Reports
Edge devices operating in dynamic environments critically need the ability to continually learn without catastrophic forgetting. The strict resource constraints in these devices pose a major challenge to achieve this, as continual learning entails memory
Fatima Tuz Zohora, Vedant Karia, Nicholas Soures, Dhireesha Kudithipudi   +3 more
doaj   +1 more source

Neuromorphic Spintronics

Neuromorphic spintronics combines two advanced fields in technology, neuromorphic computing and spintronics, to create brain-inspired, efficient computing systems that leverage the unique properties of the electron's spin. In this book chapter, we first introduce both fields - neuromorphic computing and spintronics and then make a case for neuromorphic
Atreya Majumdar, Karin Everschor-Sitte
openaire   +2 more sources

Energy Efficient Neuromorphic Computing with beyond-CMOS Oscillatory Neural Networks

, 2021
International audienceOscillatory Neural Networks (ONNs) are non-von Neumann architectures where information is encoded in phase relations between coupled oscillators. In this work, we present the concept of ONN based on beyond-CMOS devices to reduce the
Delacour, Corentin, Carapezzi, Stefania, Boschetto, Gabriele, Todri-Sanial, Aida   +3 more
core   +2 more sources

Halide Perovskites for Neuromorphic Computing

, 2023
The next generation of neuromorphic computing, which is related to emulating the neural structure and operation of the human brain, will extend into areas that correspond to human cognition, such as interpretation and autonomous adaptation.
Anthopoulos, Thomas D., Nazeeruddin, Mohammad Khaja, Vasilopoulou, Maria, Mohd Yusoff, Abd Rashid bin, Chai, Yang, Davazoglou, Konstantinos, Noh, Yong-Young   +6 more
core   +1 more source

Integrated Field‐Free SOT Domain‐Wall Synapses and MTJ Stochastic Neurons for Hardware Boltzmann Machines

Advanced Functional Materials, EarlyView.
Field‐free spin‐orbit torque domain‐wall synapses integrated with stochastic MTJ neurons enable compact hardware Boltzmann machines. Leveraging intrinsic stochasticity and multi‐level conductance, the system achieves efficient probabilistic learning with high accuracy, demonstrating a scalable spintronic platform for energy‐efficient edge AI.
Aijaz H. Lone, Meng Tang, Camelia Florica, Bin He, Jingkai Xu, Selma Amara, Georgian Melinte, Xixiang Zhang, Gianluca Setti   +8 more
wiley   +1 more source

Hyperdimensional decoding of spiking neural networks

Neuromorphic Computing and Engineering
This work presents a novel spiking neural network (SNN) decoding method, combining SNNs with hyperdimensional computing (HDC). This decoding method is designed to achieve high accuracy, high noise robustness, low inference latency and low energy ...
Cedrick Kinavuidi, Luca Peres, Oliver Rhodes   +2 more
doaj   +1 more source

Historical Foundation and Practical Guideline for Ferroelectric Switching Kinetic Studies

Advanced Functional Materials, EarlyView.
The P and U pulses in the conventional PUND measurements are not identical because of the interplay between switching current and the measurement circuit components. This circuit effect can lead to a shift in polarization transients and misinterpreted physics in the switching kinetics.
Yi Liang, Pat Kezer, John T. Heron
wiley   +1 more source

Neuromorphic Sequential Arena: A Benchmark for Neuromorphic Temporal Processing

Proceedings of the Thirty-ThirdInternational Joint Conference on Artificial Intelligence
Temporal processing is vital for extracting meaningful information from time-varying signals. Recent advancements in Spiking Neural Networks (SNNs) have shown immense promise in efficiently processing these signals. However, progress in this field has been impeded by the lack of effective and standardized benchmarks, which complicates the consistent ...
Xinyi Chen, Chenxiang Ma, Yujie Wu 0002, Kay Chen Tan, Jibin Wu   +4 more
openaire   +2 more sources

Electro‐Steric Ion Confinement in Polyelectrolyte Networks for Robust Nonvolatile Artificial Synapse

Advanced Functional Materials, EarlyView.
Polyelectrolyte stoichiometry governs ion transport and retention in electrolyte‐gated synaptic transistors. A PSS‐rich network creates electro‐steric ion confinement that suppresses ion back‐diffusion and stabilizes channel doping, enabling robust nonvolatile synaptic memory, linear weight updates, and low‐energy operation.
Donghwa Lee, Jinbo Kim, Myeongjin An, Jisoo Park, Junho Sung, Eunsung Hwang, Suhwan Seok, Seong‐Min Bak, Jeonghun Kim, Eunho Lee   +9 more
wiley   +1 more source

The more the merrier: running multiple neuromorphic components on-chip for robotic control

Neuromorphic Computing and Engineering
It has long been realized that neuromorphic hardware offers benefits for the domain of robotics such as low energy, low latency, as well as unique methods of learning.
Evan Eames, Priyadarshini Kannan, Ronan Sangouard, Philipp Plank, Elvin Hajizada, Gintautas Palinauskas, Lana Amaya, Michael Neumeier, Sai Thejeshwar Sharma, Marcella Toth, Prottush Sarkar, Axel von Arnim   +11 more
doaj   +1 more source